demonstration of data/information fusion concepts for ....../¶objectif de ce rapport est la...

Demonstration of data/information

fusion concepts for airborne maritime

surveillance operations

P. ValinA. JouanÉ. BosséDRDC Valcartier

Defence R&D Canada – ValcartierTechnical Report

DRDC Valcartier TR 2004-283May 2006

Demonstration of data/information fusionconcepts for airborne maritimesurveillance operations

P. ValinA. JouanÉ. BosséDRDC Valcartier

Defence R&D Canada - ValcartierTechnical ReportDRDC Valcartier TR 2004-283

May 2006

Author

Pierre Valin

Approved by

Éloi BosséSection Head, Decision Support Systems

Approved for release by

Gilles Bérubé

Chief Scientist

This report is the third in a series of three reports summarizing the results of PWGSC ContractNo. W7701-6-4081 on Real-Time Issues and Demonstrations of Data Fusion Concepts forAirborne Surveillance (Dr. Pierre Valin, Principal Investigator) and PWGSC Contract No.W2207-8-EC01, Demonstrations of Image Analysis and Object Recognition Decision Aidsfor Airborne Surveillance (Dr. Alexandre Jouan, Principal Investigator), under the ScientificAuthority of Dr. Éloi Bossé. The other two reports are entitled Information Fusion Conceptsfor Airborne Maritime Surveillance and C2 Operations (DRDC Valcartier TM 2004-281),and Airborne Application of Information Fusion Algorithms to Classification (DRDCValcartier TR 2004-282).

© Her Majesty the Queen as represented by the Minister of National Defence, 2006

© Sa majesté la reine, représentée par le ministre de la Défense nationale, 2006

DRDC Valcartier TR 2004-283 i

Abstract

The objective of this report is to demonstrate the achieved performance of judiciously selecteddata/information fusion and object recognition algorithms for realistic sensor simulations inrelevant airborne maritime surveillance missions. The test bed architecture is based on aknowledge-based system (KBS) developed by LM Canada in collaboration with DRDCValcartier. The chosen multi-sensor data fusion (MSDF) and image support module (ISM)algorithms are agents on this data-driven KBS. Complete results are presented for theMaritime Air Area Operations (MAAO) and Direct Fleet Support (DFS) scenarios for avariety of complicating factors, such as countermeasures, dense target environment, miss-associations, ISM classifier errors, etc. Several scenario variants are examined to ascertainthe advantages of using ISM reports and selected use of ESM reports including an electro-magnetically silent version. Dempster-Shafer evidential reasoning for identity (ID) estimationis thus tested to the fullest, and is found to handle these types of conflicts well. Finalconclusions are presented and suggestions for future research are made.

Résumé

objectif de ce rapport est la démonstration de la performance obtenue pour un choixjudicieux d algorithmes de fusion et de reconnaissance d objets, avec des simulations decapteurs réalistes dans des scénarios typiques de missions de surveillance maritime.

architecture du banc d essai est basée sur un système à base de connaissances développé parLM Canada en collaboration avec RDDC Valcartier. Les algorithmes utilisés par la fusion dedonnées multicapteurs et le module de support à l imagerie sont des agents de ce système àbase de connaissances. Des résultats exhaustifs sont présentés pour deux scénarios desurveillance maritime dits MAAO et DFS (voir le résumé en anglais ci-dessus) dans le cadrede facteurs qui compliquent le traitement, tels que la présence de contre-mesures, unenvironnement dense de cibles, des erreurs dans l association et des erreurs dues au MSI, etc.Plusieurs variantes des scénarios sont explorées afin de vérifier l utilité des rapports du MSI et

utilisation restreinte des rapports ESM, y compris le cas d un silence électromagnétiquecomplet. Le raisonnement évidentiel de Dempster-Shafer pour l identification des cibles estainsi testé complètement et il est démontré qu il se comporte bien en présence de tellessources de conflit. Des conclusions sont ensuite présentées et des suggestions de recherchesfutures sont émises.

ii DRDC Valcartier TR 2004-283

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DRDC Valcartier TR 2004-283 iii

Executive summary

The objective of this report is to demonstrate the achieved performance of judiciously selecteddata/information fusion and object recognition algorithms for realistic sensor simulations inrelevant airborne maritime surveillance missions. Given the scenarios examined, a single-scan associator, such as a nearest-neighbour algorithm or a Jonker-Volgenant-Castanonvariant of it, is chosen and a single Kalman filter track update mechanism is performed for thepositional fusion component. The identity is obtained through an identification fusion doneby a truncated Dempster-Shafer algorithm with near-optimal truncation parameters, and aminimum for the ignorance, which ensures recovery under countermeasures.

The test bed architecture is based on a knowledge-based system (KBS) developed by LMCanada in collaboration with DRDC Valcartier. The chosen multi-sensor data fusion (MSDF)and image support module (ISM) algorithms are agents on this data-driven KBS. A completedesign of both the MSDF and the ISM is presented, with all types of objects clearly identified,namely: data types, algorithmic agents and context functions. In particular, the functionalcomponents of the MSDF are clearly delineated into registration (or alignment), association,state (or position) update and identity update components, as per the previous reports.Similarly, the ISM components are detailed and automatic vs. operator-controlled versions arehighlighted.

Complete results are presented for the two main scenarios described in the first report of thisseries (DRDC Valcartier TM 2004-281):

• Maritime Air Area Operations (MAAO), and• Direct Fleet Support (DFS)

and for a variety of complicating factors, such as• countermeasures by enemy ships,• dense target environment, resulting in• miss-associations,• ISM classifier errors, etc.

Several scenario variants are examined to ascertain the advantages of using ISM reports andselected use of ESM reports, including a completely electro-magnetically silent version.

Dempster-Shafer evidential reasoning for identity (ID) estimation is thus tested to the fullest,and is found to handle these types of conflicts quite well. The randomness of each scenariorun is ensured particularly for ESM reports, and typical results are presented.

Finally, conclusions are stated, and suggestions for future research are made, including multi-scan (or multi-frame) association and the use of banks of filters, as well as studying differentmeasures of performance, particularly for ID estimation. It should be recognized that the newsensors procured under the Aurora Incremental Modernization Project (AIMP) could simplifysome MSDF processing while posing new challenges as well.

Valin, P., Bossé, É, Jouan, A. (2006). Demonstration of data/information fusion concepts forairborne maritime surveillance operations, DRDC Valcartier TR 2004-283.

iv DRDC Valcartier TR 2004-283

Sommaire

objectif de ce rapport est la démonstration de la performance obtenue pour un choixjudicieux d algorithmes de fusion et de reconnaissance d objets, avec des simulations decapteurs réalistiques dans des scénarios typiques de missions de surveillance maritime. Etantdonné la complexité des scénarios choisis, le choix d un algorithme d association, tel que leplus-proche-voisin ou la variante Jonker-Volgenant-Castanon de celui-ci, a été choisi pourchaque retour radar, suivi d une mise à jour positionnelle par un filtre Kalman unique.

identité de la cible est déterminée par une version tronquée de l algorithme de Dempster-Shafer avec un choix presque optimal des paramètres de troncation et un minimum pour

ignorance qui assure le rétablissement approprié en présence de contre-mesures.

architecture du banc d essai est basée sur un système à base de connaissances développé parLM Canada en collaboration avec RDDC Valcartier. Les algorithmes utilisés par la fusion dedonnées multicapteurs et le module de support à l imagerie sont des agents de ce système àbase de connaissances. La conception détaillée des composantes de fusion de données multi-capteurs et de module de support à l imagerie est présentée, en identifiant clairement lesdifférents types d objets, tels que les données elles-mêmes, les agents algorithmiques et lesfonctions de contexte. En particulier, les composantes fonctionnelles sont décomposées enalignement, suivies de l association, puis des composantes de mise-à-jour de la position et de

identité, tel qu expliqué dans les précédents rapports.

Des résultats exhaustifs sont présentés pour deux scénarios dits MAAO et DFS (voir lerésumé en anglais ci-dessus) décrits dans le premier rapport de cette série (DRDC ValcartierTM 2004-281) dans le cadre de facteurs qui compliquent le traitement, tels que

• la présence de contre-mesures,• un environnement dense de cibles, pouvant mener à• des erreurs dans l association,• des erreurs dues au module de support à l imagerie, etc.

Plusieurs variantes des scénarios sont explorées afin de vérifier l utilité des rapports dumodule de support à l imagerie et l utilisation restreinte des rapports ESM, y compris le cas

un silence électromagnétique

Le raisonnement évidentiel de Dempster-Shafer pour l identification des cibles est ainsi testécomplètement et il est démontré qu il se comporte bien en présence de telles sources deconflit. Le côté aléatoire de chaque instantiation des scénarios est assuré, particulièrement ence qui concerne les rapports ESM, et des résultats typiques sont présentés.

Des conclusions sont ensuite identifiées et des suggestions de recherche future sont émisesnotamment une association basée sur une histoire de plusieurs retours radar, l utilisation debanques de filtres Kalman, de même que l étude de différentes mesures pour l évaluation dela performance, particulièrement en ce qui concerne l identité. Il faut reconnaître que lesnouveaux capteurs acquis dans le cadre du Aurora Incremental Modernization Project (AIMP)pourraient simplifier certains aspects de MSDF, mais en créer d autres.

Valin, P., Bossé, E, Jouan, A. (2006). Demonstration of data/information fusion concepts forairborne maritime surveillance operations, DRDC Valcartier TR 2004-283.

DRDC Valcartier TR 2004-283 v

Table of contents

Abstract...................................................................................................................................i

Executive summary .............................................................................................................. iii

Sommaire..............................................................................................................................iv

Table of Contents ...v

List of figures .......................................................................................................................vii

List of tables..........................................................................................................................ix

1. Introduction ...............................................................................................................1

2. Scenarios and simulations ..........................................................................................3

2.1 Scenarios.......................................................................................................3

2.2 Non-imaging sensor simulations ....................................................................3

2.2.1 Radar model .....................................................................................3

2.2.2 ESM model.......................................................................................4

2.2.3 IFF model .........................................................................................5

2.2.4 Link model .......................................................................................5

2.3 Imaging sensor simulations............................................................................5

2.3.1 Ship motion model............................................................................5

2.3.2 Declarations from the SSAR ISM to MSDF ......................................8

2.4 Platform database ..........................................................................................9

2.4.1 Actual fuzzification rules used ........................................................11

2.4.2 Generic proposition construction.....................................................14

3. Measures of Performance.........................................................................................16

3.1 Positional fusion..........................................................................................16

3.2 Identity information fusion ..........................................................................16

4. Implementation........................................................................................................18

4.1 KBS test bed architecture ............................................................................22

4.2 KBS implementation of algorithms..............................................................24

vi DRDC Valcartier TR 2004-283

4.2.1 MSDF s four basic functions...........................................................24

4.2.2 SAR ISM........................................................................................26

Fully automated design ...................................................................26

Operator-in-the-loop design ............................................................28

5. Results.....................................................................................................................30

5.1 MAAO........................................................................................................30

5.1.1 ISM results .....................................................................................30

5.1.2 DFDM-3 results ..............................................................................32

ID of Russian ships without the SSAR ISM.....................................35

ID of Russian ships with the SSAR ISM .........................................41

ID of Russian Ships with few ESM reports......................................47

ID of Russian ships in electro-magnetically silent scenario..............51

Conclusions for the MAAO scenario...............................................57

5.2 DFS.............................................................................................................58

5.2.1 ISM results .....................................................................................58

5.2.2 Identification of the American fleet through ESM and SAR ISM.....60

5.2.3 Identification of Canadian fleet at short range through ESM............63

5.2.4 Conclusions for the DFS scenario ...................................................68

5.3 Other scenarios............................................................................................69

5.3.1 ISM results for the counter-drug operations scenario .......................69

5.3.2 Maritime sovereignty patrol scenario...............................................70

6. Conclusions and suggestions for future research.......................................................71

7. References ...............................................................................................................74

8. Acronyms ................................................................................................................77

9. Annexes...................................................................................................................81

9.1. General data/information fusion sources ......................................................81

9.2. Specific related data/information fusion sources ..........................................82

9 Distribution list ........................................................................................................85

DRDC Valcartier TR 2004-283 vii

List of figures

Figure 1. Parameter definition for ship motion ........................................................................7

Figure 2. Example of image generated by SARSIM for a ship (Adams) experiencing roll .......8

Figure 3. Fuzzy membership functions for speed ..................................................................11

Figure 4. Functional diagram of the MSDF test-bed.............................................................19

Figure 5. A representation of an identity information fusion process ....................................20

Figure 6. Test-bed application interactions...........................................................................22

Figure 7. High-level design of the MSDF test-bed and the KBS BB environment..................24

Figure 8. Design for the MSDF agent decomposition on the KBS BB ...................................25

Figure 9. Original design for the SAR ISM on the KBS BB ..................................................27

Figure 10. Integration of an ISM for SAR data processing into the MSDF loop.....................29

Figure 11 SAR images of the Destroyer UDALOY, the Cruiser KARA and the FrigateMIRKA.........................................................................................................................30

Figure 12 Temporal evolution of typical propositions related to the MIRKA-II ....................36

Figure 13. Temporal evolution of the proposition of highest mass in the MIRKA-II case .....37

Figure 14. Temporal evolution of typical propositions related to the UDALOY-II................38

Figure 15. Temporal evolution of the proposition of highest mass in the UDALOY II case ..39

Figure 16. Temporal evolution of typical propositions related to the KARA-AZOV.............40

Figure 17. Temporal evolution of the proposition of highest mass in the KARA-AZOV case41

Figure 18. Temporal evolution of typical propositions related to the MIRKA-II with SSAR.42

Figure 19. Temporal evolution of the proposition of highest mass in the MIRKA-II case withSSAR............................................................................................................................43

Figure 20. Temporal evolution of typical propositions related to the UDALOY-II with SSAR44

Figure 21. Temporal evolution of the propositions of highest mass in the UDALOY-II casewith SSAR ....................................................................................................................45

viii DRDC Valcartier TR 2004-283

Figure 22. Temporal evolution of typical propositions for the KARA-AZOV with SSAR ....46

Figure 23. Temporal evolution of the proposition of highest mass in the KARA-AZOV casewith SSAR ....................................................................................................................47

Figure 24. Temporal evolution of typical propositions of high mass for the MIRKA-II ........48

Figure 25. Temporal evolution of typical propositions of high mass for the KARA-AZOV ..49

Figure 26. Temporal evolution of typical propositions of high mass for the UDALOY-II .....51

Figure 27. Time evolution of the generic identification of the MIRKA-II from the SAR ISMonly...............................................................................................................................53

Figure 28. Time evolution of generic identification of UDALOY-II from the SSAR ISM only55

Figure 29.. Time evolution of generic identification of KARA-AZOV from the SAR ISM only57

Figure 30. SAR images for the American ships COONTZ, TICONDEROGA and VIRGINIA58

Figure 31. ID time evolution for the COONTZ .....................................................................61

Figure 32. ID time evolution for the TICONDEROGA .........................................................62

Figure 33. ID time evolution for the VIRGINIA ..................................................................63

Figure 34 ID time evolution for the Frigate HALIFAX ........................................................65

Figure 35 ID time evolution for the Destroyer IROQUOIS ..................................................66

Figure 36 ID time Evolution for the frigate IMPROVED RESTIGOUCHE..........................67

Figure 37 ID time evolution for the Support Ship IMPROVED PROVIDER........................68

Figure 38 SAR image of the vessel QUEST.........................................................................69

DRDC Valcartier TR 2004-283 ix

List of tables

Table 1. Sea State (SS) parameters..........................................................................................6

Table 2 List of emitters on SURFACE platforms .................................................................34

Table 3. Propositions appearing for the frigate MIRKA-II....................................................36

Table 4. Propositions appearing for the destroyer UDALOY-II ............................................38

Table 5. Propositions appearing for the cruiser KARA-AZOV .............................................40

Table 6. Propositions appearing for the frigate MIRKA-II...................................................41

Table 7. Propositions appearing for the destroyer UDALOY-II ............................................43

Table 8. Propositions appearing for the cruiser KARA-AZOV .............................................45

Table 9. SSAR ISM declarations and resulting generic identification for the MIRKA-II........52

Table 10 SAR ISM declarations and resulting generic identification for the UDALOY-II ....54

Table 11. SAR ISM declarations and resulting generic identification for the KARA-AZOV.56

Table 12. Emitter list for the American fleet ........................................................................61

Table 13 Emitter list for the Canadian fleet ..........................................................................64

x DRDC Valcartier TR 2004-283


DRDC Valcartier TR 2004-283 1

1. Introduction

The first report of this series entitled Information Fusion Concepts for Airborne MaritimeSurveillance and C2 Operations addressed the problem of multi-source data fusion on the CP-140 (Aurora) airborne maritime surveillance aircraft. To that end, a survey of the conceptsthat are needed for data/information fusion was made, with the aim of improving commandand control (C2) operations. All the current and planned sensors were described and theirsuitability for fusion discussed. Relevant missions for the aircraft were listed and the focuswas placed on a few important ones that make full use of the Aurora s sensor suite. For theidentity information component of MSDF, a comprehensive set of a priori databases wasconstructed that contained all the information/knowledge about the platforms likely to beencountered on the missions. The most important of these was the platform database (PDB),which lists all the attributes that can be measured by the sensors (with accompanyingnumerical or fuzzy values), and these can be of three types: kinematic, geometrical or directlyin terms of the identity of the target platform itself.

The second report of this series entitled Airborne Application of Information FusionAlgorithms to Classification surveyed the positional fusion algorithms and the reasoningframeworks common in the artificial intelligence field for identity information fusion, andselected those which are appropriate to deal with dissimilar data coming from sensorsinvolved in airborne data/information fusion. The image support module (ISM) for theexisting forward looking infra-red (FLIR) made use of many of these reasoning frameworksin parallel, and actually fused the results coming from these complementary classifiers. TheISM for the upcoming spotlight synthetic aperture radar (SSAR) also incorporated some ofthese reasoning methods in a hierarchical manner to provide multiple inputs to the multi-sensor data fusion (MSDF) module. The data used to train, validate and test the ISMs was acombination of simulated and real imagery for the SSAR and unclassified airborne data forthe FLIR.

The objective of this final report is to demonstrate the achieved performance of judiciouslyselected data/information fusion and object recognition algorithms for realistic sensorsimulations in relevant airborne maritime surveillance missions. Given the scenariosexamined, a single-scan associator such as a nearest-neighbour algorithm or a Jonker-Volgenant-Castanon variant of it (Jonker & Volgenant, 1987; Drummond, Castanon &Bellovin, 1990) is chosen, and a single Kalman filter track update mechanism (Bar-Shalom &Fortmann, 1988) is performed for the positional fusion component. The identity is obtainedthrough an identification fusion done by a truncated Dempster-Shafer algorithm with near-optimal truncation parameters, and a minimum for the ignorance, which ensures recoveryunder countermeasures.

The test bed architecture is based on a knowledge-based system (KBS) developed by LMCanada in collaboration with DRDC Valactier. The chosen multi-sensor data fusion (MSDF)and image support module (ISM) algorithms are agents on this data-driven KBS. A completedesign of both the MSDF and the ISM is presented, with all types of objects clearly identified,namely: data types, algorithmic agents and context functions. In particular, the functionalcomponents of the MSDF are clearly delineated into registration (or alignment), association,

2 DRDC Valcartier TR 2004-283

state (or position) update and identity update components, as per the previous reports.Similarly, the ISM components are detailed and automatic vs. operator-controlled versions arehighlighted.

This report is organized as follows:

• Section 2 reviews the scenarios and the extent of the simulation for both non-imagingand imaging sensors.

• Section 3 discusses the measures of performance by which the MSDF algorithmscould be judged, and selects a few for presentation later in the report.

• Section 4 presents complete results for the Maritime Air Area Operations (MAAO)and the Direct Fleet Support (DFS) scenarios for a variety of complicating factors,such as: countermeasures by enemy ships, dense target environment, resulting in mis-associations, ISM classifier errors, etc. Dempster-Shafer (DS) evidential reasoningfor identity (ID) estimation is thus tested to the fullest, and is found to handle thesetypes of conflicts well. The randomness of each scenario run is ensured particularlyfor ESM reports, and typical results are presented. Partial results are also shown forother scenarios described in the first report of this series (DRDC Valcartier TM 2004-281).

• Section 5 presents final conclusions and suggestions for future research.

It should be noted that several scenario variants are examined to ascertain the advantages ofusing ISM reports and selected use of ESM reports, including a completely electro-magnetically silent version. In addition, some results are presented for the Counter-DrugOperations scenario.


2. Scenarios and simulations2.1 ScenariosLet us recall that four scenarios were originally designed (see the first report of the series):

1. Maritime Air Area Operations (MAAO) involving a Russian fleet;

2. Direct Fleet Support (DFS), involving support to both a Canadian and anAmerican fleet;

3. Counter Drug-Operations; and

4. Maritime Sovereignty Patrols.

The first two scenarios make extensive use of SSAR imagery and the first one also involvescountermeasures. For the purposes of this study, all the relevant fusion functions areexercised in these two scenarios, from easy association and tracking tasks to difficult ones,and from easy imagery tasks to atypically hard ones. As a general rule, one can classify thesetwo scenarios by their geometry and by order-of-magnitude estimates of their difficulty.

The MAAO scenario will eventually see the Russian fleet broadside from a 100 km standoffdistance with easily separable tracks for each ship. Radar returns thus allow for easy tracking.The ships are also sufficiently well separated for the ESM, whose bearing accuracy is greaterthan the radar s but is a classified number (representative numbers were put in the simulator).Because this scenario seems too easy, countermeasures were put on one Russian ship (theUDALOY-II) to test the robustness of the DS evidential reasoning scheme.

The DFS scenario is more challenging. The parameters of the scenario show a Canadian fleetwhose ships are roughly separated by 0.5 km at a 20 km distance when viewed broadside, foran angular spread of 0.025 radians or 1.4 degrees (well within the 0.24 degrees of bearingaccuracy for the radar). The classified ESM bearing accuracy is also just good enough topermit good ESM to track associations. However, the equivalent parameters for the Americanfleet are 0.5 km at 100 km, or 0.28 degrees, very comparable to the radar s bearing accuracy.This will challenge the association of radar contacts with tracks, but will be even morechallenging for the ESM-to-track association mechanism, since the ESM bearing error islarger the radar s. One should thus expect several such mis-associations, which is equivalentto the countermeasures in the MAAO scenario.

In all the scenarios, the geometry of the fleets whose components have to be identifiedchanges throughout the flight profile of the Aurora, so the above considerations are based onthe best-case geometry of broadside view. In the case of the American fleet, such a broadsideview only occurs late in the DFS scenario.

2.2 Non-imaging sensor simulations

2.2.1 Radar modelThe CASE-ATTI (Roy, Bossé & Dion, 1995) sensor module provides excellent radarsimulations (Duclos-Hindié et al., 1995). For the AN/APS-506 radar the followingparameters were used:


• scan rate (rpm): 6

• minimum range: 11,196 m

• maximum range: 300,000 m

• search beam elevation: 0 degrees

• search beam elevation width: 15 degrees

The minimum range for the radar is calculated from the Aurora s altitude and the maximum15-degree depression angle.

2.2.2 ESM modelSince CASE-ATTI cannot model passive sensors at the moment, the AN/ALR-502 ESM ismodelled as an artificial sensor with the following parameters:

• scan rate (rpm): N/A

• minimum range: 0

• maximum range: 250,000 m

• search beam elevation: 0 degrees

• search beam elevation width: 90 degrees

The scan rate will be varied in different scenarios, in order to change the relative importanceof ESM declarations in obtaining the desired identification of a given platform. Note that therange selected is within the radar s, so ESM reports will be correlated to existing tracks in allcases.

A random number generator selects from the list of emitters corresponding to the platform,ensuring that each Monte-Carlo run of a given scenario can generate different results,allowing for some statistically significant data to be obtained.

When the CASE-ATTI file contains emitters for a given platform that do NOT match the listin the PDB for the same platform, this corresponds to that platform employingcountermeasures to defeat our identification algorithms (in our case, a truncated DS).

It should be noted that a true functioning ESM sensor intercepts electronic signals in thefrequency band(s) of interest. The parameters of the intercepted signal are measured.Waveform type is one of the measured signal parameters. If the signal is a pulsed emitter,other characteristics such as pulse duration and pulse repetition interval will also be measured.The ESM sensor maintains a list of the signals that it expects to intercept in the environmentand the parameter ranges over which each signal is expected to operate. Some sensors usedistributions to represent parameter values while other sensors simply store minimum andmaximum values. To each signal type, a five-character alphanumeric electronic intelligence(ELINT) notation (ELNOT) is designated. ELNOTs are assigned by the intelligencecommunity to the various signals. The ESM sensor compares the measured parametersagainst the values in the database for each ELNOT to determine whether the detected signalcould have been generated by that ELNOT. If so, the detected signal is said to "match" theELNOT in the database.


The measured data may not match a single entry; alternatively it may match just one, ormultiple entries in the database. In instances where there are multiple matches, the ESMsensor should output all the matching entries. When the measured data matches more thanone entry in the database, some sensors output a "confidence factor" for each such entry basedon the stored probability distributions for each parameter. When the confidence factors arederived in this manner, they are proportional to the probability that the sensor declaration (i.e.,match to that ELNOT) is made, given that the detected signal was generated by the emitterrepresented by that ELNOT.

The crude simulation by an artificial sensor selecting just one of the platform s radars istherefore inadequate in the true situation where many ELNOTs are declared with associatedconfidence values.

2.2.3 IFF modelSince the AN/APX-502 IFF is slaved to the AN/APS-506 radar, it has the same parametervalues.

2.2.4 Link modelLink-11 reports were occasionally fused at selected intervals, mainly for ID confirmation,since the time tags tend to be stale.

2.3 Imaging sensor simulationsSAR imagery is provided by the SARSIM2 package from DRDC Ottawa with suitablemodifications, as described in this section. SARSIM2 uses a physical optics approximationfor the radar cross-section (RCS) estimation and generates a very clean image. Thesemodifications had to be presented prior to showing the performance of the SAR ISM onsimulated imagery in section 4.2.1 of the second report of this series. In essence, threeartificial image degradation algorithms have been added in order to generate images of morerealistic appearance. These are:

a. Pixel spreading (local random swapping of pixels);

b. Pixel blurring to simulated non-ideal detector response;

c. Speckle noise (a multiplicative noise following a Gamma distribution withnumber of looks = 3).

These degradations are performed sequentially over a local 3x3 window, just after the RCSsimulation and before saving the image to disk. The degradation parameters have been setempirically such that simulated image texture qualitatively resembles XDM image texture.While the degradation of the near-perfect imagery from SARSIM2 ensured realism of theimagery sent to the SARSIM, it was up to the target segmentation Step 1 of the ISM to cleanup the image for further processing.

2.3.1 Ship motion modelThe ship motion model used in SARSIM assumes small displacement during the illuminationtime. Each motion component Ω is modelled by periodic motion


( )Ω Ω Ω∆Ω∆

∆ Ω ∆= ≈ ==

o ot

ott

t tsin ( )ω ω0

where Ωο and ω are the amplitude and frequency of the motion, respectively. Severalcomments are in order:

a. Factor Ωοω is the motion rate parameter (in rad/sec for roll and pitch and inm/sec for heave) used in SARSIM (motion.dat input file).

b. This is of course a very crude model. Real sea surface oscillation is not asingle frequency oscillation process. However we can assume, in a firstapproximation, that the sea surface oscillates with a mean frequency ω thatcan be derived from the measured sea surface spectrum (Newman, 1977, &Miles, 1969) or from wind speed, assuming the sea surface follows thePierson-Moskowitz spectrum for fully developed sea as shown below inTable 1. In this table, H1/3 (m) is the significant wave height (i.e., the averageof the highest one third of all waves), ω (rad/s) is the mean frequency (i.e.,expected number of times the wave amplitude passes through zero withpositive slope), and λ(m) is the mean wavelength based on the dispersionrelation for a fluid of infinite depth ( 2/2 ωπλ g= ).

Table 1. Sea State (SS) parameters

Beaufort Wind Scale Physical wave parameters derived from thePierson-Moskowitz spectrum

SS knots m/s H1/3 (m) ω (rad/s) τ (s) λ (m)

0 0-1 0-0.52 0-0.006 >23.3 0-0.27 0-0.11

1 1-3.5 0.52-1.80 0.006-0.069 23.3-6.7 0.27-0.94 0.11-1.37

2 3.5-6.5 1.80-3.35 0.069-0.24 6.7-3.6 0.94-1.75 1.37-4.7

3 6.5-10.5 3.35-5.41 0.24-0.62 3.6-2.24 1.75-2.80 4.7-12.3

4 10.5-16.5 5.41-8.50 0.62-1.54 2.24-1.42 2.80-4.42 12.3-30.5

5 16.5-21.5 8.50-11.07 1.54-2.62 1.42-1.09 4.42-5.76 30.5-51.8

6 21.5-27.5 11.07-14.16 2.62-4.28 1.09-0.85 5.76-7.39 51.8-85.2

However, because the Pierson-Moskowitz model tends to overestimate ω forlow sea state (SSs), it is better to calculate ω from real sea wave spectra(Miles, 1969).

c. Parameter ω is the apparent wave frequency as measured by an observerstationed on the ship (Figure 1). It is related to ship heading with respect towave propagation direction (φ), ship forward velocity (V), averagewavelength (λ ), as well as average sea wave frequency (ω ), according to


ω ωπ

λφ= −

2 V cos

d. Amplitude Ωο depends on the rigid body and inertial properties of the ship aswell as the driving force (i.e., the SS). In particular, Ωο should vanish forSS=0. In this work, Ωο will be estimated with the approximate modelpresented in (Tunaley, 1981), which will not be reviewed here and is assumedvalid. The old FORTRAN code listed in that document was recoded toprovide roll, pitch and heave as well as average wave frequency ω as afunction of the sea wave spectrum, forward ship speed, angle φ and shipdisplacement, length, beam and draft. This is shown in Figure 1 below.

e. Moving ships can still experience roll and pitch in SS=0 due to their naturaloscillation frequency.

λ

λroll =λ/cos φ

λpitch =λ/sin φ

wave direction

φ

Figure 1. Parameter definition for ship motion

A typical generated image is shown in Figure 2 below.


Roll Rate(rad/s)

Roll Rate(rad/s)

-0.002

0.002

-0.005

0.005

-0.01

0.01

-0.02

0.02

Figure 2. Example of image generated by SARSIM for a ship (Adams) experiencing roll

Further modifications to SARSIM have been performed in order to correctly simulate imagesof rotating ships. As for the SAR portions of SARSIM, we have had to increase the size ofmany buffers in order to simulate images with a range resolution smaller than 1 m/pix.Artificial image degradation routines have also been added.

The main issue was the lack of target aspect information in the simulated images provided bythe original SARSIM program. It seems that the antenna altitude was not correctly taken intoaccount. In particular, the image generated by the SAR portion of SARSIM and the inverseSAR (ISAR) portions with no target rotations was not the same (note that SARSIM resultsfrom the integration of many small programs provided to DRDC-O through many differentcontracts). We have made (more or less intuitive) corrections to the ISAR routines in order toresolve this issue.

Heave motion was not taken into account in the original SARSIM. We have added thisfeature within the approximate ship motion model used in SARSIM.

2.3.2 Declarations from the SSAR ISM to MSDFNote that the design for the SSAR ISM shown in the second report of this series providesthree inputs to the DS evidential reasoning component for ID within the MSDF module:


1. First, a ship length (SL) is determined (and an interval around it estimated) and sent toMSDF.

2. Second, a preferred ship category (SC) is determined and the probabilities of the threecategories are sent to the DS evidential reasoning scheme in MSDF, as masses thatsum up to 1: line combatant, merchant or unrecognized.

3. Third, if the ship is a line combatant, a Bayes classifier returns the probabilities ofeach of the 5 ship types (ST), which are sent to the DS evidential reasoning scheme inMSDF, with masses that sum up to 1: frigate, destroyer, cruiser, battleship, or aircraftcarrier.

In the later section devoted to results, the acronyms SL, SC and ST will denote these threeinputs, respectively.

2.4 Platform databaseAs a preamble, one should be aware that the format in which the databases should be encodedis an active topic of research, with experts still debating whether relational databases orobject-oriented (OO) databases are preferable. Compromises must often be made, and the useof commercial off-the-shelf (COTS) software (SW) may in the future make as many inroadsas COTS hardware (HW) is currently doing throughout the world s military establishments.This section only describes the content of these databases, not their optimal architecture norhow complex their interrelations are.

The first report of this series contained a detailed discussion of the content of databasesneeded for reasoning at Level 1, as well as Levels 2 and 3. It will be briefly summarized herein the context of reasoning frameworks that have to treat the information contained therein forthe purposed of identity (ID) estimation. For ID estimation to be properly achieved in Level 1DF, all possible attributes that can be measured by all sensors must be listed in the platformdatabase (PDB). They can be divided into three groups (Valin & Bossé, 2003b):

1. Kinematic attributes: which can be estimated through tracking in the positional estimationfunction of DF, and through reports from IFF and datalink. The maximum acceleration, themaximum platform speed, the minimum platform speed and the cruising speed either serveas bounds to discriminate between possible air target IDs or suggest the most plausible IDs.The maximum altitude that a platform may reach can serve as a bound for altitude reportedby the IFF or deduced from a tracker in 3-D from sensor reports in 2-D.

2. Geometrical attributes: which can be estimated by algorithms which post-process imaginginformation from sensors such as FLIR, or Electro-Optics (EO) and SAR. Classifiers thatperform such post-processing can be thought of as ISMs performing much the samefunction as the ESM does for the analysis of electromagnetic signals. These ISMs need thethree geometrical dimensions of height, width and length (for FLIR and EO), and also theRCS of the platform seen from the front, side and top (for the SAR and radiometric radar).In addition, the distribution of relevant features may be needed for classifiers, but may beconsidered part of the algorithms that generate plausible IDs.

3. Identification attributes: which can be directly given by the ESM or as outputs of the FLIRand SAR ISM. The ESM requires an exhaustive list of all the emitters carried by theplatform. However, the ISMs are usually designed to not only provide the best single ID


possible but also to estimate confidence in higher levels of an appropriate taxonomy tree(discussed in the next section). This taxonomy tree is usually derived from some standard,either STANAG 4420 or MIL-STD 2525 (A or B), or can even be customized dependingon the application (naval, airborne or helicopter platforms).

Some sensors measure attributes quite directly. For example, the ESM will provide an emitterlist with some confidence level about the accuracy of the list that reflects the confidence in itselectromagnetic spectral fit. However, an IFF response can lead to an identification of afriendly or commercial target but the lack of a response does not necessarily indicate that theinterrogated platform is hostile. One has to distribute the lack of a response between at leasttwo declarations: the most probable foe declaration and a less probable friendly or neutraldeclaration that allows for IFF equipment that is not working or absent.

Some complications arise when dealing with kinematic parameters reported occasionally bythe tracker in positional estimation. First, each physical quantity has a different dimension(speed, acceleration) and an accurate determination is not necessarily needed for fusion.Indeed, it is convenient to bin the attribute speed into fuzzy classes like very fast, fast,average, slow and very slow (separately for air and surface targets).

The same can be done for length in bins of 40-metre width, for example. Membership in eachclass is a measure of how well the measured value fits into the descriptor as described below.Thus fuzzification is necessary even though fuzzy logic may not be used as a reasoningframework. Similarly, defuzzification may be used to present results to the operator throughthe human computer interface (HCI).

Other complications arise in DF with respect to correlating tracker speed values with thespeed attribute in the PDB. Indeed, speed reports must be fused only if they involve asignificant change from past behaviour in that track. The reason is two-fold. First, no singlesensor must attempt to repeatedly fuse identical ID declarations, otherwise the hypothesis thatsensor reports are statistically independent is violated. Second, the benefits of the fusion ofmultiple sensors is lost when one sensor dominates the reports. Furthermore, a measuredvalue of speed only indicates that the target is capable of that speed, not that it corresponds toeither the maximum or minimum speeds listed in the PDB. It is a reasonable workinghypothesis to fuzzify the value reported by the tracker into adjacent bins to account for thetarget being at, say, only 80% of optimal speed (a very fast target can occasionally travelfast ), or travelling with a strong tailwind (a fast target can appear as very fast ). Finally,

the concept of binning can be generalized to continuous membership functions of a fuzzy set.

An example of a 5-term decomposition into triangular membership functions is shown inFigure 3 below for the physical attribute of air speed (Valin & Bossé, 2003a):

1. VS = very slow,

2. S = slow,

3. M = medium,

4. F = fast,

5. VF = very fast.

The overlapping regions measure the fact that experts need not agree on a precise definition oflinguistic terms describing speed.


AIR SPEED

MEM

BER

SHIP

VS S M F VF

Figure 3. Fuzzy membership functions for speed

In general, all physical attributes require fuzzification for proper treatment by evidentialreasoning.

2.4.1 Actual fuzzification rules usedThe optimal fuzzification scheme depends naturally on the range of values that these physicalparameters can attain for the targets listed in the PDB. This subsection attempts to bin thevalues of the physical parameters so that each bin contains roughly the same number ofplatforms for the maximum value of the physical parameter.

For speed v, the chosen fuzzification is as follows:

a. For surface targets:

1) very slow: v < 20 knots2) slow: 20 ≤ v < 25 knots3) medium: 25 ≤ v < 30 knots4) fast: 30 ≤ v < 35 knots5) very fast: v ≥ 35 knots

b. For air targets:

1) very slow: v < 200 knots2) slow: 200 ≤ v < 400 knots3) medium: 400 ≤ v < 600 knots4) fast: 600 ≤ v < 800 knots5) very fast: v ≥ 800 knots


Note that the speed attribute is more important for air targets than for ships. Hence one canexpect that it is more relevant to the CPF version of the Data Fusion Demonstration Model(DFDM) than to airborne sensors measuring principally surface target characteristics.

For acceleration a measured in g, the chosen fuzzification for air targets is as follows:

a. very small g a < 0.5 gb. low: 0.5 g ≤ a < 1.5 gc. medium: 1.5 g ≤ a < 3 gd. high: 3 g ≤ a < 5 ge. very high: a ≥ 5 g

Acceleration is best measured in interacting multiple models (IMMs), which contain aconstant acceleration model. The present implementation of the Kalman filter consists ofestimating a noise value for the acceleration parameter from a linear regression on the recenthistory of the track. Since this estimation is only very gross, the acceleration attribute is notfused at the moment.

For ship length as measured by the ISM, the PDB is divided into bins of 40 m width centred at100, 140, 180, 220, 260, 300, 340, 380, 420 and 460 m. The large values are only relevant foraircraft carriers and merchant ships such as tankers. Since very few merchant ships are in thePDB at the moment, these large values correlate strongly with aircraft carriers.

Since the altitude parameter is only relevant for air targets, the fuzzification of this parameterand its attribute fusion is not contemplated. The IFF altitude report is currently only used togenerate the propositions which can be inferred from the tracking. It is actually planned tofuzzify this attribute in a manner similar to the one used for speed.

For RCS values measured in square decimetres (dm2), there are several existing guidelines.

For example, Nathanson (1991) chooses only three RCS classes for air targets based ondecreasing RCS_FOR:

a. LARGE: for heavy bombers, 707 or DC-8 size jets. This corresponds in theX-band (the case for the AN/APS-506) to RCS_FOR or nose RCS of 2,800,but with an RCS_SID or broadside RCS of 30,000

b. MEDIUM for attack bombers, 727 or DC-9 size jets. This corresponds in theX-band to RCS_FOR or nose RCS of 400, but with an RCS_SID or broadsideRCS of 80,000

c. SMALL for fighters or 4-passenger jets. This corresponds in the X-band toRCS_FOR or nose RCS of 120, but with an RCS_SID or broadside RCSranging from 2,000 to 6,500.

Note than the MEDIUM class actually has a larger RCS_SID than a LARGE aircraft. Toavoid such confusion, our nomenclature will have individual definitions for five (not three)classes for each of the following: RCS_SID, RCS_FOR and RCS_TOP.


The range of values taken by naval targets is quite different from air targets and spans a muchlarger range, from 2 dm2 for a small open boat, 200 for a small pleasure boat, 10,000 for apatrol boat, 100,000 for a freighter, and up to 100,000,000 for fully loaded cargo ships. It isthus clear that fuzzification must be different for air and naval targets.

There are also many empirical rules that apply for generic air and naval targets that are noteasily applicable to a detailed database such as our PDB. For example, there is a simpleapproximation that relates RCS_FOR to the square of the length of an aircraft (acomplementary dimension) and another approximation for naval targets that relatesgeneralized RCS to the power 3/2 of the ship displacement in kilotons (a very indirectlyrelated quantity to actual ship size). Neither of these types of rules is appropriate for our PDBand in fact are fairly often violated.

The chosen fuzzification for air targets is therefore as follows (all values in dm2):

a. For RCS_SID:

1) very small: RCS_SID < 8002) small: 800 ≤ RCS_SID < 5,0003) medium: 5,000 ≤ RCS_SID < 10,0004) large: 10,000 ≤ RCS_SID < 20,0005) very large: RCS_SID > 20,000

b. For RCS_TOP:

1) very small: RCS_ TOP < 8002) small: 800 ≤ RCS_TOP < 5,0003) medium: 5,000 ≤ RCS_ TOP < 20,0004) large: 20,000 ≤ RCS_ TOP < 60,0005) very large: RCS_ TOP > 60,000

c. For RCS_FOR:

1) very small: RCS_ FOR < 202) small: 20 ≤ RCS_ FOR < 2003) medium: 200 ≤ RCS_ FOR < 2,0004) large: 2,000 ≤ RCS_ FOR < 10,0005) very large: RCS_ FOR > 10,000

Similarly, the chosen fuzzification for surface targets is therefore as follows:

d. For RCS_SID:

1) very small: RCS_SID < 1,0002) small: 1,000 ≤ RCS_SID < 10,0003) medium: 10,000 ≤ RCS_SID < 100,0004) large: 100,000 ≤ RCS_SID < 1,000,0005) very large: RCS_SID > 1,000,000


e. For RCS_TOP:

1) very small: RCS_ TOP < 20,0002) small: 20,000 ≤ RCS_ TOP < 100,0003) medium: 100,000 ≤ RCS_ TOP < 500,0004) large: 500,000 ≤ RCS_ TOP < 1,000,0005) very large: RCS_ TOP > 1,000,000

f. For RCS_FOR:

1) very small: RCS_ FOR < 2,0002) small: 2,000 ≤ RCS_ FOR < 8,0003) medium: 8,000 ≤ RCS_ FOR < 20,0004) large: 20,000 ≤ RCS_ FOR < 40,0005) very large: RCS_ FOR > 40,000

In incoherent mode operation of the radar, the RCS is in principle calculable from the signal-to-noise ratio (SNR) given by CASE_ATTI. It is also calculable in coherent mode operationfrom the total intensity of pixels belonging to the target after illumination by N pulses over theimage acquisition time. Neither of these calculations is actually performed at the moment.

2.4.2 Generic proposition constructionWhenever a new physical attribute is measured by a sensor, or a new fuzzified speed value isdetected by the tracker (indicating a manoeuvre likely to identify the target), a genericproposition is sent to the identity information fusion component of MSDF. The content ofthese generic propositions is generated during the initialization phase to speed up MSDFitself.

In its current implementation, about 189 generic propositions are generated, with the details:TYPE 20

SUB-TYPE 60

ALLEGIANCE 5

COUNTRY 26

LANGUAGE 24

OFFENSIVENESS 7

SIZE 16

VELOCITY 20

LENGTH 11

The propositions related to size, velocity and length correspond to the fuzzification alreadydescribed in the preceding section.

The various generic propositions for platform type built at the time of initialization associatethe known set of platforms to their type. Some additional generic propositions are provided


by the analysis of SAR images (LINE, MERCHANT, OTHER), or correspond to the union ofother existing generic propositions (SURFACE, AIR, SURFCOM).

The naval version of the MSDF program had to fuse positional and ID data characterizingtargets of type AIR. While the missions of the CP-140 Aurora are mostly devoted to maritimesurveillance (i.e., tracking and identification of targets of type SURFACE), they also need tobe able to track any target of type AIR which may appear in its environment. Consequentlyone must add the capability of discriminating AIR (SURFACE) from SURFACE (AIR)targets.

The discrimination algorithm we used is based on the following two remarks:

a. Altitude can only be truly evaluated by an IFF interrogation in mode C.b. Targets of type AIR have a velocity greater than targets of type SURFACE.

With these two remarks some discrimination rules can be implemented.

It is also assumed that no platform of type SURFACE or HELO can go faster than the slowestplatform of type AIR. This is a working hypothesis that can be refined later if necessary.

One can also build generic propositions for the platform allegiance: UNKNOWN, PENDING,FRIEND, NEUTRAL, FOE, where PENDING has been added to simulate an uncertainty onthe determination of allegiance due to an incomplete IFF response (for example, afterreceiving an improper number of pulses).


3. Measures of Performance

The naval domain, particularly the Halifax class frigates, was the first to have MSDFconsidered as a software upgrade, in parallel with possible hardware upgrades. The AdvancedShipborne Command and Control Technology (ASCACT) test bed, and its follow-onCommand Decision Aid Technology (COMDAT) demonstrator programs had to definemeasures that could quantify the improvements of the addition of MSDF relative to theexisting Command and Control System (CCS). It is instructive to review the measures ofperformance (MOPs) that were retained by DRDC Valcartier (Bossé & Roy, 1997), and selectsome that are appropriate to the airborne domain. The MOPs fall in three main areas, namely

1. reaction time,

2. track quality, and

3. ID accuracy.

Track quality refers only to positional fusion, while ID accuracy concerns only identityinformation fusion, but reaction time affects both.

3.1 Positional fusionThe positional reaction time MOPs can be decomposed into target detection, targetconfirmation and target deletion within the volume of interest (VOI), with the latter relatedmostly to track management (time interval for dead-reckoning).

The positional track quality MOPs are all closely related and concern track estimation (radialmiss distance, state estimation error and accuracy of the filter-calculated covariances), trackpurity (good correlation vs. mis-associations), and track management statistics.

In the case of airborne surveillance, the targets are ships that are rather well separated (as in afleet), and have very little manoeuvrability (when compared with the radar sampling rate).Positional fusion MOPs are therefore of little importance for MSDF-equipped airborneplatforms, looking mostly at naval targets, but of considerable importance for MSDF-equipped naval platforms, looking mostly at air targets.

3.2 Identity information fusionFor a vulnerable airborne platform such as the Aurora, the identity of targets is far moreimportant. The CP-140 has the means of attaining this ID through two imaging sensors, theSSAR and the FLIR, whose main uses are to detect ships at long range (SSAR) and at shortrange (FLIR) and to classify them in categories (line combatants vs. merchants), and types.

The reaction time MOP for positive and correct ID is crucial for both naval and airborneplatforms. It is especially relevant for a defenceless CP-140 that must identify enemy shipsbefore coming too close to jeopardize its survivability.

Time constraints are of course related to range constraints through the aircraft's possiblycomplex trajectory (in the case of avoidance of suspect targets), so the concepts of range of"Unknown" ID, range of "Correct" ID, and range of "Positive ID" are also important. Each of


these ranges are defined when the accumulated evidence supporting a given ID exceeds acertain threshold. How one determines relevant thresholds is a matter to be settled by militarypeople and fusion experts together.

When one uses DS theory, a single diagram (per target of interest) can convey all the relevantinformation if one plots the highest belief (or mass) with its corresponding (possibly complex)associated proposition as a function of time. It is then up to the user to define the thresholdsto be used. Since the scenarios usually have a simple trajectory for the Aurora (rectilinear orcircular), this conveys appropriate information for both time and range MOPs.

Also, when the scenario has evolved sufficiently that many DS propositions are available, andwhen a decision needs to be made as to a single ID, many possibilities are available to thedecision maker: pignistic probability (which equally redistributes the mass in a compoundproposition to each of its members), plausibility (which measures the lack of evidencecontradicting the ID), or simply highest mass.

Another MOP for ID, called the Bayesian percent attribute miss (BPAM), could also be usedsince the natural frequencies of the targets are known, i.e., the ground truth is known from thescenario. Since the number of interesting targets is small in all the scenarios, only a fewBPAMs would need to be calculated as a function of time. However, since we are interestedin knowing the exact composition of the proposition with the highest mass at any timethroughout the scenario for each target, and a BPAM graph for a given target does not conveythis information, BPAM graphs will not in addition be computed, in order to limit the size ofthis report.

Since each scenario is run many times in different conditions (density of targets,countermeasures, frequency of ESM reports, SAR imagery classifier outputs on simulatedimagery, etc.), this report will only consider one graph per target, showing the highest mass(and its associated proposition) as a function of time, with important fusion events that affectthe ID of the target annotating the graphs (e.g., ESM and SAR reports).


4. Implementation

In the functional diagram of the MSDF test bed described in Figure 4 below (Bossé, 2000),the sensor data coming from the various sensors on board the platforms involved processed bythe scenario generator are generated by the CASE-ATTI sensor simulator. Requirements onthe performance and accuracy of the sensor simulator data therefore translate into additionaldemands on the functionality of that simulator. An important first step (denoted by acheckmark in Figure 4) is input data preparation, which includes the computation of allgeneric compound propositions which one may want to evaluate (e.g., any level of the

taxonomy of the PDB, allegiance, etc.), usually done off-line before MSDF starts to minimizethe CPU constraints. Input data preparation for imagery consists in Level-0 pre-processingfor the classifier and generating the classifier reports, e.g., SL determination, SC and STdeclarations. For imaging sensors, which are cued by existing interesting tracks, associationis already made, so the classifier results go directly to the identity information fusioncomponent of MSDF.

The generic data fusion application of Figure 4 below must contain the following set ofsequential functions to act on simulated data: registration, association, positional fusion andidentity information fusion (Roy & Bossé, 1998). Since the role and scope of each of thesefunctions depend considerably on the type of data that it processes, a brief example is given ineach case:

1. Registration or data alignment first performs spatial and temporal alignment of thesimulated sensor data. This is crucial when dealing with stale Link-11 data but ofrelatively minor importance when accurate time tagging and Global PositioningSystem (GPS) information is available.

2. An association mechanism then correlates the new incoming data with possibleexisting tracks found in the internal system track data store (ISTDS, also check-marked in Figure 4), and sends associated positional data to positional fusion andassociated attribute data (e.g., image features of a given target) to information fusion.This associator must be able to deal with clutter and false contacts spanning manyorders of magnitude, e.g., due to sea states 0-7. This function is also checkmarked inFigure 4 because of its importance later in the analysis of the scenarios (particularlyfor the ESM).

3. Positional or kinematic fusion then updates the tracks in the time domain with theassociated new data and writes this positional information to the ISTDS, possiblyextracting attribute data such as speed and acceleration and sending to informationfusion. The complexity of the Kalman filtering involved in maintaining a stable trackdepends a lot on the target manoeuvres, since re-initialization of new tracks must beavoided, yet track covariance must not be overly large.

4. Identity information fusion (or estimation) then fuses all attribute data throughevidential reasoning, whether they originate from imaging (through imageunderstanding and feature extraction) or non-imaging sensors, and consequentlyupdates the ISTDS. Depending on possible electronic countermeasures orcamouflaging of images, the selected algorithm must deal with various degrees of


conflict (including total conflict) and maintain an adequate level of ignorance. Sincethis is the MSDF function of prime importance in maritime surveillance, it is alsocheckmarked in Figure 4. In order to reduce complexity in the DS evidentialreasoning scheme, some pruning of irrelevant propositions will be done (through awell-defined, optimized and benchmarked truncation mechanism) before storage inthe ISTDS.

Figure 4. Functional diagram of the MSDF test-bed

This all-important identity information fusion component can be detailed further as in Figure5 below.

1. The first task of the identity information fusion process is to compare the attributeinformation and identity declarations obtained from various sources with a PDBideally containing all the possible identity values that the potential target may take.

2. The second task of the identity information fusion function is the data associationprocess, which determines to which MSDF track the received sensor informationbelongs. This is a critical process of any fusion function. Following the conclusionsof the data association process, the identity proposition construction should becompleted with the attributes derived from the positional fusion (speed, acceleration,etc.).


3. The fusion rules are then applied to fuse the MSDF target track identity propositionslist with the input propositions. Decision rules are then applied for pruning the"unessential" propositions, selecting the "best" identity propositions. A set of identitypropositions is then stored in ISTDS.

4. The last step in the identity information fusion process is the refinement of theidentity propositions. Refinement of the identity propositions can be achieved byreasoning in terms of groups or organizations of objects. This is done in theperception refinement process of the higher levels of data fusion (Paradis, Roy andTreurniet, 1998) and illustrated as part of the integration process of Figure 5.

Figure 5. A representation of an identity information fusion process

Initial Identity Proposition Construction(Fuzzification , alignment, correction, )

Association(Correlation)

Completion of IdentityProposition Construction

PDB

NewNewInput DataInput Data

ISTDSFusion Rules

(e.g. Dempster -Shafer ,Bayesian ,Possibilistic )

CurrentCurrentSystemSystem Tracks Tracks

Emitter Listing

Geopolitical Listing

Platform Characteristicsand Emitter List

PDB

Prop. #1: Friend 80%Prop. #1: Friend 80%Prop. #2: Ignorance 20%Prop. #2: Ignorance 20%e.g.

Prop. #1: Air 70%Prop. #1: Air 70%Prop. #2: Ignorance 30%Prop. #2: Ignorance 30%

e.g.

Decision Rules/Propositions Management(e.g. utility theory, game theory)

UpdatedUpdatedSystem TrackSystem Track

Higher Levels Data Fusionand Resource Management

(Doctrine, history data, STANAGS, sensor management, communications, ..etc)

Prop. #1: Air & FriendProp. #1: Air & Friend 56%56%Prop. #2: AirProp. #2: Air 14%14%Prop. #3: FriendProp. #3: Friend 24%24%Prop. #4: IgnoranceProp. #4: Ignorance 6% 6%

e.g.

Integration/managem

entIn

tegr

atio

n/m

anag

emen

t

Information Sources

Organic/ non-organicImaging/ non-imaging

Input data preparation


This section will describe:

• which algorithms were selected from those in the second report of this series,

• which architecture these algorithms were implemented on, and

• what results were achieved for the two main scenarios described in the first report ofthis series (and limited results for a third).

Figure 6 depicts the high-level test bed system for a single platform (LM Canada, 2001b).The HCI application is composed of two components: the system manager and the runtimedisplay. The system manager s role is to set up the test bed applications by selectingCommand and Control Information System (CCIS) application configurations, establishingrun-time display configuration, selecting stimulation (STIM) files as input to the STIM driverapplication, selecting scenarios or sensor models as input to the CASE-ATTI application, etc.The run-time display component is used to display the estimated tactical picture and otherrelevant outputs received from the various applications while a simulation is running. Therun-time display component also manages the archiving of data to the performance analysisdata store.

In order to achieve the real-time constraints and to increase flexibility by providing the abilityto run the applications on different workstations, communications between applications arebased on the Berkeley sockets connection oriented client server protocol. Messagesexchanged between the various test bed applications developed are defined and included inthe messaging library as shown in Figure 6.

Figure 6 also presents at the bottom the socket client server connection relation for the variousapplications for the single platform test bed. In Figure 6 the server-client connection of theHCI application pertains to the run-time display component. The selection of server-clientconnection determines the order in which the applications must be started. For the singleplatform test bed, the system manager component of the HCI starts the applications in thefollowing order:

a. CCIS applicationb. sensor model applicationc. STIM driver application.


STIMFILES

PADBFILES

Messages

Messages

Messages

STIM DriverApplication

HCIApplication

MSDFApplication

ADP-InetSocket

ADP-InetSocket

ADP-InetSocket

ADP-InetSocket

ADP-InetSocket

ADP-InetSocket

MessagingLibrary

ADP ConfigFiles

PlatformDatabase

MSDFParameter

File

ADP-InetSocket

CASE-ATTIApplication

ADP-InetSocket

CASE-ATTIFILES

CASE-ATTI

STIM Driver HCI

MSDF orCCIS

Applications

PADB

S

S

S

S

S

S = Server SocketC = Client Socket

C

CC

C

C

Figure 6. Test-bed application interactions

4.1 KBS test bed architectureA real-time KBS that supports distributed processing has been constructed on the BlackBoard(BB) paradigm. These systems have been used extensively in the past for computer vision,planning and scheduling, data fusion or command and control systems. In such a BB


environment, knowledge is generally encapsulated in the form of sets of procedures orproduction rules (if-then statements), or as semantic nets that describe a network of objectsand their relationships through inheritance. The knowledge sources (algorithms, contextualfacts, logical rules or heuristics) contribute pieces of information by modifying certaincontrols or data structures on the BB. BB systems offer highly structured opportunisticproblem-solving.

Because of the large size of the BB and the type of problem-solving chosen, the proposedsolution is one but not necessarily the only solution. It is thus important to exercise the BBKBS as much as possible with different scenarios (just as neural nets need to be trained).Thus the KBS BB test bed was designed, evaluated, modified and refined in a spiraldevelopment cycle over many years of trying out scenarios with various algorithmicimplementations and parameter definitions.

At various stages of this spiral development, open literature presentations were made to thedata/information community at large, for the purposes of getting feedback. Thesepublications highlight different features of the KBS BB test bed, catered to the audiencepresent at each conference. A chronological list of such conference presentations, in whichthe co-authors were involved, indeed show an increasing level of sophistication andperformance through the years 1997-1999:

1. (Shahbazian, Gagnon, Duquet, Macieszczak & Valin, 1997)

2. (Jouan, Gagnon, Shahbazian & Valin, 1998)

3. (Shahbazian, Bossé, Gagnon, Macieszczak & Valin, 1998)

4. (Shahbazian, Duquet, Macieszczak & Valin, 1998)

5. (Valin, Gagnon, Macieszczak, Shahbazian & Bossé, 1998)

6. (Shahbazian, Duquet & Valin, 1998)

7. (Valin, P., & Jouan, A., 1999).

8. (Jouan, Valin & Bossé, 1999)

9. (Bossé, Valin & Jouan, 1999)

10. (Valin, Jouan & Bossé, 1999)

11. (Valin, Jouan, Gagnon & Bossé, 1999).

Gathering elements from all these presentations, together with the results of the second report,resulted in an hour-long retrospective invited presentation of the whole system developed byLM Canada and DRDC Valcartier (Valin & Bossé, 2003a).

The complete KBS BB design incorporates all of the following elements:

a. The four MSDF functions themselves as multi-agents on the KBS BB,

b. The imagery classifiers as agents on the KBS BB.

The CASE-ATTI sensor simulation package will interact with the BB through a socket asshown in Figure 7 below (LM Canada, 2001a).


Global Controller

BB Controller

KBS BB Environment

Data FusionAgents

• Registration• Association by JVC

Position fusion by Kalman filteringID by truncatedDempster- Shaferreasoning

Black B

oard

Flight DataSocket

ImagingDecision Aidse.g.4-step SARhierarchicalclassifier

LogPerformanceEvaluation

HCI

SensorSimulationScenario

Generation

TargetSimulation

RadarIFF

ESMLink-11SAR

Socket

•

•

Figure 7. High-level design of the MSDF test-bed and the KBS BB environment

As this report was being written, a data management system (DMS) was contracted out toGeneral Dynamics as part of the Aurora Incremental Modernization Program (AIMP). Theabove test bed is deemed sufficiently general in its architecture to encompass the real DMSthat is being installed on the Aurora. The main requirement from the DMS for any MSDF isthat digitized data be available on a common bus, as was the case for the Halifax class frigates(SHINPADS bus). The possibility of tapping that bus, which can be done by hardware (acard) or by software (a gateway), ensures that either contact or track data (position andidentity) will be available to the MSDF function without interfering with other applicationsrunning on that bus.

4.2 KBS implementation of algorithms

4.2.1 MSDF’s four basic functionsThe overall MSDF BB design is displayed in Figure 8 below (LM Canada, 2000), with thefour basic functions clearly delineated.


CONTACT_XXX

DUMMY*

RAW_CONTACT

CONTACT_BUFFER CreatePairs*

ASSIGNMENT

AttributeGating*

NN /JVC

NULL

Principal

CONTACT

DeleteContact* AddContact*AlignXXX*

CONTACT_TRACK_PAIR

TIME_UPD_XXX

TimeUpdateXXX*

NULLPROPOSITIONCREATE_XXX_TRACK

POS_UPD_XXX_XXX

ASSIGN_XXX_XXXFindTrack*

GateXXX*CreateXXXTrack*

PosUpdateXXX*

IdentityUpdate* DeletePair*

TRACK_STATE TRACK

FuseProposition*

GenerateTrack*

SocketContact

KillAssessment

TrackGenID

GENERIC_PROP_POOL

MakeGenPool

Registration Association State Estimation ID Estimation

Figure 8. Design for the MSDF agent decomposition on the KBS BB

The four basic functions of MSDF are denoted by different line types and decomposed intosmall modular intelligent agents.

a. Registration by dash-dotted linesb. Association by dashed linesc. State estimation by continuous linesd. Identity estimation by dotted lines.

Three types of objects are present on the KBS BB and denoted by different symbols:

a. Data types are denoted by circlesb. Algorithmic agents are denoted by rounded rectanglesc. Context functions are denoted by elongated rounded rectangles.

Agents may be activated during the execution of the entire application or may be activated/de-activated on demand. In the former they are activated in the initialization phase ready to be


executed, in the latter they are activated/de-activated by other agents. As we mentioned above,we reduced to the minimum the use of cross-agent activation.

An active agent is ready to be executed but it can take a while before it is actually executed.It will be executed only when the specific data-type it has to act upon is instantiated on theblackboard as an active data-type.

The data-type carries the numerical variables needed by an agent-embedded algorithm. Data-types can be instantiated, modified or extracted from the blackboard by agents. A data-typemay also carry some contextual attributes that are needed to control the sequential nature ofthe operations when several active agents may be executed on it. The value taken by theseattributes are probed by context-functions.

The context-functions are made up of a sequence of IF-THEN statements. They can also beused to select specific agents at the initialization phase by probing the value of configurationparameters (i.e., the chosen association algorithm, the chosen tracker).

4.2.2 SAR ISMThe agent-based SAR ISM module currently consists of a hierarchical procedure for shipfeature extraction and classification. This module comprises the following four main blocks:

a. An association procedure ( gating ) which will associate the image or anequivalent extracted chip (also known as region of interest) to a track numberin the track database managed by the MSDF module.

b. A database of generic propositions which correlates the extracted attributes toa mission-specific database of platforms.

c. The hierarchical procedure for ship classification.

d. A set of configuration files which contain various parameters needed by theclassifier, such as the results of neural network training and the probabilitydensity functions needed for the estimation of the ship type.

Fully automated designThe four global steps of the ISM design, including the hierarchical procedure for shipclassification, were decomposed into more fundamental algorithms. The original design ofthe SAR ISM considered full automation of the ISM process in much the same way as theMSDF agents operate in a sequential manner without operator intervention as shown in Figure9 (LM Canada, 2000).


Principal

RAW_CONTACT

NN_WEIGHTSLoadNNWeights

SocketContact

DUMMY

CreateSARImage

SAR_IMAGE

ROI

ExtractSARShipROI

SARShip_LENGTH

SARShip_Denoising

ROI_LABELS

HOUGH_DAT

HoughTransform

SARShip_Segmentation

SARShip_CAT

SARShip****

CLASS

Apply****ClassNNBackprojector

ApplySARShipTypeBayes

Classifier

SARShipTYPE

CONTACT_TRACK

_PAIR

FuseSARShipLength

FuseSARShipCategory

SARShipSCAT_DIST

AlignSARShipScatDist

ApplyRulesforSARShipCategory

FuseSARShip****Class

FuseSARShipType

EstimateSARShipLength

DiscretizeSARScatDist

CancelSARShipLength

DUMMY

CancelSARShipCategory DUMMY

ROI_LIST

AddROI

DeleteROINULL

Figure 9. Original design for the SAR ISM on the KBS BB

The SAR ISM is currently made of a hierarchical classifier specialized in automatic shiprecognition. The proposed design provides tools for an easy implementation of various imagemanipulations and algorithms to perform pre-processing (image format conversion,segmentation, smoothing, filtering), descriptions and feature extraction (textures, objects), andclassification.

However, experience with the classifier has shown that the ISM can occasionally makemistakes, e.g., the cruiser VIRGINIA is mis-identified as a destroyer in the DFS scenario.Since processing SAR imagery is done only on a very occasional basis, it was judged that theoperator should have some control in sending the hierarchical information to the MSDFmodule. Such control cannot be given to the operator (except through rare manual operatorentry of additional information) for the four MSDF functions since the sensor data rate is toohigh. This decision is made in the spirit of the SAR ISM being considered as a decision aidtool for airborne surveillance (DAAS).


Operator-in-the-loop designThe initial design was therefore revised to give the operator complete control over the varioussuccessive steps of processing, i.e., to put the operator in the decision loop. The operatorauthorizes or vetoes the execution of tasks by sending instructions through the humancomputer interface (HCI). By offering such control to the operator, this design does not useall the possibilities offered by an agent-based implementation. The HCI also serves to startimage data acquisition through the simulator. The HCI operator plays a key role in acceptingor rejecting the fusion of information coming from each of the steps of the ISM:

a. Length determination (in particular, choice of rejection if the value is toosmall),

b. Category determination (in particular, choice of rejection if the imagedisplayed has unusual scatterer distribution),

c. Type discrimination (in particular, if operator-recognized type from scattererdistribution and the result does not match the Bayes length classifier output).

The integration of the SAR ISM within the actual data fusion test bed is given in Figure 10(LM Canada, 2001a). In this figure, the operator sitting at the HCI starts a simulationscenario (Start). During the scenario, he sends a request for the acquisition of a SAR/ISARimage by virtually locking the SAR antenna on a given target (Acquire Image of LockedTarget). Then, if the acquisition conditions are met (generation of SAR images at a fixedsquint angle of 90 degrees), the data fusion and the scenario simulator send the necessaryinformation to the SAR simulator. Once the simulation is completed, the image is displayedon the HCI (Display). The operator may then decide to execute the various steps of thehierarchical ship classifier (Extract Features) and fuse the output result. There are currentlythree steps to be executed sequentially: estimation of the ship length, estimation of the shipcategory and estimation of the ship type. At the end of each of these steps, the operator maydecide to create identity declarations and fuse them with the propositions already accumulatedon the locked target.


Scenariosimulator

SAR-ISM

HCI

Start

Extract Features

State Estimation

Alignment

Identity Estimation

TrackDatabase

Contact-Track

pair

Fuse ifGating

OK

Update

Refresh

Fuse IdentityDeclarations

SARsim

Acquire Image oflocked target

Display

Figure 10. Integration of an ISM for SAR data processing into the MSDF loop


5. Results

5.1 MAAO

5.1.1 ISM resultsThe MAAO scenario contains imagery for a fleet of three Russian ships, which are imagedwhile the Aurora is looking directly at its side and the ships are moving with a relative bearingangle of 45 degrees. This restriction is due to the use of DRDC-O s SARSIM simulator. It isexpected that equivalent imagery could be obtained from a SAR working in squint mode (i.e.,not side-looking) as long as the relative angle between the SAR slant range and the targetvelocity bearing is the same.

Figure 11 (Lockheed, 1998) shows the raw SAR imagery in reverse video and histogramequalized (on top), the segmented image with its extracted centreline by the Hough transformand the thresholded major scatterers for the destroyer UDALOY, the cruiser KARA and thefrigate MIRKA (respectively from left to right). The images are not necessarily to scale.

Figure 11 SAR images of the Destroyer UDALOY, the Cruiser KARA and the Frigate MIRKA

Note that the SAR acquisition parameters are:

a. aircraft altitude: 3 km

b. range to target: 100 km


c. aircraft speed: 0.15 km/sec (300 knots)

d. wavelength: 0.03 m

e. ship heading: 45 degrees

f. slant-range resolution: 0.75 m

g. cross-range resolution: 2.0 m

For each of the three imaged ships, the ISM s hierarchical classifier generates three attributesin succession, each of which leads to several identity declarations for Line ships, with the %in the figure above translated directly into associated masses in the Dempster-Shafer sense:

a. First the length is obtained after centreline detection from the cleaned-upbinary image;

b. Next the Line category is declared with its confidence level obtained bykeeping the top 10% of the strongest pixels;

c. Finally the Line type is declared, from a choice of five Line types: frigate,destroyer, cruiser, battleship or aircraft carrier.

Details about the SSAR classifier outputs can be found in the second report of this series.

For the destroyer UDALOY (Lockheed, 1998):

a. The apparent target length is 140 m for an interval of ship length declarationof 133 - 179 m.

b. The masses for the ship category declaration are: Line 0.86; Merchant 0.05;Unknown 0.09.

c. Finally the masses for Line ship type are: Frigate 0.08; Destroyer 0.448;Cruiser 0.29; Battleship 0.0; Aircraft Carrier 0.01, summing up to 0.86.

Note that the ISM correctly identifies the UDALOY as a destroyer, a fact that will helpcounter the wrong ESM report on the same target.

For the cruiser KARA (Lockheed, 1998):


b. The masses for the ship category declaration differ only very slightlydepending on whether one uses the centreline or the range profile: Line 0.81(0.80); Merchant 0.06; Unknown 0.13 (0.14).

c. Finally the masses for Line ship type are: Frigate 0.0; Destroyer 0.1; Cruiser0.67; Battleship 0.0; Aircraft Carrier 0.04, summing up to 0.81.


Note that the ISM correctly identifies the KARA as a cruiser.

For the frigate MIRKA (Lockheed, 1998):

a. The apparent target length is 71 m for an interval of ship length declaration of66 - 102 m. This rather small ship length indicates either:

1) a small ship2) a front view3) a bad segmentation4) a bad ship end-points detection.

This usually means that the ship identity declaration cannot be trusted. In thiscase, however, the MIRKA is indeed a small ship.

b. A 10% percentage of the strongest pixels is kept, resulting in masses for theship category: Line 0.86; Merchant 0.05; Unknown 0.09.

c. Finally the masses for Line ship type are: Frigate 0.86; with all others 0.0

Note that the MIRKA is correctly identified as a frigate by the ISM.

It should be noted that the ship category percentages (from 81% to 86%) are virtuallyidentical for each ship (in this scenario as well as others to follow) because the chosen shipsbelong very obviously to the Line category. In addition, the masses for the ship categorydeclaration differ only very slightly (less than 1%) depending on whether one uses thecentreline or the range profile decompositions.

5.1.2 DFDM-3 resultsBy design, the MAAO scenario has relatively easy tracking and would have easy ID ifcountermeasures were not introduced for one of the ships, the UDALOY.

The MAAO scenario is meant

1. to verify that the SSAR ISM works perfectly when the ships are very distinct, sothat the Bayes length classifier works well;

2. but more importantly to test whether the DS theory of evidence can recover fromcountermeasures. DS theory is particularly suited for our application because itrequires no a priori information, can resolve conflicts (present in hostileenvironments due to countermeasures), and can assign a mathematical meaning toignorance (which is the result of some of the chosen algorithms).

Note that DS offers a powerful approach to managing the uncertainties within the problem oftarget identity. Every sensor declaration about the M possible values of an attribute assignsa basic probability assignment (BPA or mass) value mi (i=1...M) to that attribute (present in


the database) and generates M propositions, which are just the numerical list of platforms inthe PDB that can attain the said value for the attribute.

For a PDB containing N platforms, the numerical list of platforms, which forms a proposition,is represented in the current implementation by a string of zeroes and ones in the location of astring of N bits. This is done to speed up calculations by bit manipulations for ensembleoperations such as union and intersection, which are needed in DS theory. For physicalquantities like speed, length and RCS, and image classification attributes like category orclass, M is usually greater than 1. This is due either to the fuzzification of the physicalquantity or because of the inherently complex nature of the algorithmic determination of theattribute (e.g., by neural net outputs).

However, traditional DS has the major inconvenience of being an NP-hard problem. Asvarious items of evidence are combined over time, DS combination rules will have a tendencyto generate more and more propositions, which in turn will have to be combined with newinput evidence. Since this problem expands exponentially, the number of retained solutionsmust be limited. Our truncated version of the DS theory of evidence performs theconventional combination rules of DS theory but retains the final solution propositionaccording to the following criteria based first on cardinality:

a. All combined propositions that have a BPA higher than MaxBe are retained.

b. All combined propositions that have a BPA lower than MinBe are eliminated.

c. If the number of retained propositions in substep a is smaller than MaxNum,the subroutine will retain, by decreasing BPA, the propositions consisting ofone element (singleton) until MaxNum is reached. If MaxNum is notreached, one retains, by decreasing BPA, the propositions consisting of twoelements. The process is repeated for triplets, quadruplets, etc., untilMaxNum is reached.

The values for the three parameters MaxBe, MinBe and MaxNum currently utilized are 0.05,0.001 and 8, respectively. Substep c takes into consideration that the commanding officerfavours singleton propositions. With this truncation, the previously mentioned commutativitystill applies, but the associativity does not necessarily apply. When the BPAs are small andmany propositions are fused, the process loses the associative property and the results(identification) become dependent on the combination order.

From our investigation and experience, in the worst case, this violation has a small effect andchanges the calculated BPA by no more than 5% compared with the standard theory. Theworst case is the one where the MaxNum selected propositions all have a BPA of the order ofMaxBe. In such cases there is a large portion of BPA that goes to the dropped propositions.Fortunately, in practical applications, sensors provide attributes with enough evidence toavoid this situation.

The sum of the masses of the dropped propositions are assigned to the ignorance. In addition,the ignorance is never allowed to become zero, in order that the truncated DS algorithmrecovers gracefully under countermeasures. The authors are aware of many other variants forredistributing the masses of the dropped propositions, which could have been used, but


decided on this one because of its simplicity. Its first reported use in realistic scenarios datesback to a symposium that was held in 1993 but published in 1994 (Simard, Valin &Shahbazian, 1994). Two other alternatives are explained in the next few paragraphs.

At nearly the same time, Tessem (1993) also proposed three other parameters: k the minimumnumber of focal elements to be kept, l the maximum number of focal elements to be kept, andx the maximum threshold on the sum of the lost masses. It can be summarized as follows:

1. Select the k focal elements with highest masses.

2. While the sum of their masses is less than 1-x, and while their number is less than l,add the next focal element with highest mass.

Bauer (1997) proposed another scheme, which is summarized here (k is also the desirednumber of focal elements).

1. Select the k-1 focal elements with highest masses.

2. Distribute the other masses among the selected focal elements.

The distribution of the masses of the pruned focal elements lies on successive iterations andaccounts for the relations between these focal elements and the remaining ones (objects incommon, etc.). After the last iteration, the remaining mass is assigned to the frame ofdiscernment, which is then the kth focal element. The complete algorithm of mass distributionwill not be detailed here, but can be found in (Bauer, 1997).

Since it was mentioned in the first report of this series (DRDC Valcartier TM 2004-281) thatfuture PDBs will contain many more platforms (of the order of 2200, divided roughly intoabout 1500 surface and sub-surface vessels, and about 700 as airborne targets) than the oneused in this report (consisting of 138 entries), several rules-of-thumb will have to be used toscale the parameters MinBe, MaxBe and MaxNum according to the size of the PDB.

An attempt at such rules-of-thumb was performed by optimizing and benchmarking (Boily &Valin, 2000; Valin & Boily, 2000), with the results that MaxBe = 1 / (2.5 MaxNum) andMinBe = 1 / (12.5 MaxNum) seem to convey a reasonable relationship between the variables.The values mentioned above satisfy this relationship, and MaxNum has to increase as thePDB size increases, possibly linearly in the domain considered.

The countermeasures are introduced by introducing wrong emitters for the UDALOY in thescenario, as shown in Table 2, where the underlined emitters correspond to alien emitters.They do not belong, in the PDB, to the emitter list of the corresponding platform.

Table 2 List of emitters on SURFACE platforms

Platform List of emitters

UDALOY 69 97 63 65 129 91 71 93 77

KARA 78 84 62 64 85 45 92 68 46 93 104 103

MIRKA 44 55 47 56 103 109

TYPHOON 138


Let us focus our attention on the identification of the three Russian ships for two reasons:

1. Our scenario has planned to image the scene containing them and will run objectrecognition (OR) algorithms on the acquired images to extract ID propositionsassociated with these platforms.

2. One of the Russian platforms is trying to hide by employing countermeasures.

In order to properly evaluate the impact of using imaging sensors on target identityrecognition, let us first study the graphs showing the temporal evolution of the basicprobability assignments (masses) of some specific propositions related to the target to berecognized. Obviously, since the scenario is known, the correct identity is also known.

Since one is dealing with platforms of the same type (SURFACE) moving at the same speed(30 knots), it is anticipated that the ESM will play a major role in the identification. Caremust be taken to avoid fusing too many ESM reports if one wants to see the impact of fusingSAR attributes with the other non-imaging sensors.

Having made these remarks, four different versions of this scenario are presented below forthe ID of all three Russian ships:

1. without the SSAR ISM;

2. with the SSAR ISM;

3. with a substantial reduction of ESM reports;

4. with no ESM reports, but only SSAR ISM reports, an electro-magnetically silentscenario where either the Russian ships do not emit in order to remain covert orthe Aurora s ESM is malfunctioning.

ID of Russian ships without the SSAR ISMID estimation of the platform MIRKA-II

Figure 12 shows the temporal evolution of the mass associated with typical propositionsrelated to the platform MIRKA-II (Lockheed, 1998). These propositions are identified by thenumbers 1, 2, 3 and correspond to three platforms (triplet), two platforms (doublet) or oneplatform (singleton) in our database. The platforms designated by the selected propositionsare listed in Table 3 below. The propositions shown are the ones with the highest mass atsome time in the scenario, or the ones which split off from them at some point in the scenario.

At each fusion step of any ESM report, the mass associated with that report is 0.8, with therest going to the ignorance. This is the case through sections 5.1.2 and 5.2.2.


Table 3. Propositions appearing for the frigate MIRKA-II

Prop. # Platform name

1 MIRKA-II, MIRKA-I, SAM-KOTLIN

2 MIRKA-II, MIRKA-I

3 MIRKA-II

1

2

3

1 = Mirka II, Mirka I, Sam-Kotlin

2 = Mirka II, Mirka I

3 = Mirka II

triplet singleton

#44 #44#47

Figure 12 Temporal evolution of typical propositions related to the MIRKA-II

The final platform is the correct one. To understand the role of the ESM measurement in theevolution of the three propositions, Figure 12 shows a set of triangles which correspond to thereception of an ESM contact. Three of them have been coloured (highlighted) since they playa key role in the evolution of the propositions. The left-most coloured triangle corresponds toemitter #44 ( STRUT-CURVE ), which only belongs to the MIRKA-II. The consequence is


a drop of the mass of proposition 1, leaving behind the SAM-KOTLIN. The secondinteresting ESM contact corresponds to emitter #47 ( DON-2 ), which only belongs to theMIRKA-II. The consequence is a drop of the mass associated with proposition 2, leavingbehind the MIRKA-I. The third interesting ESM contact (right-most coloured triangle)corresponds again to emitter #44. The consequence is an increase of the mass associated withproposition 3. Figure 13 (Lockheed, 1998) represents the successive propositions of highestmass in the successive declarations on a scale different from Figure 12. However, itsparticular shape is entirely explained by Figure 12.

1 3

Figure 13. Temporal evolution of the proposition of highest mass in the MIRKA-II case

ID estimation of the platform UDALOY-II

Figure 14 shows the temporal evolution of the mass associated with four typical propositionsrelated to the UDALOY-II (Lockheed, 1998). These propositions are identified by thenumbers 1, 2, 3, 4 in the following table.


Table 4. Propositions appearing for the destroyer UDALOY-II


1 GRISHA-III, MIRKA-II, UGRA-II

2 MODIFIED-KIEV, UDALOY-II, UDALOY-AND-KULAKOV, UDALOY-SPIRIDONOV

3 UDALOY-II, UDALOY-AND-KULAKOV, UDALOY-SPIRIDONOV

4 UDALOY-II, UDALOY-SPIRIDONOV

1

23 4

1 = wrong tripletdue to false emitter

2 = quartet with Udaloy triplet

3 = Udaloy triplet

4 = Udaloy doubletcontaining the Udaloy II

#69#63 #63

Figure 14. Temporal evolution of typical propositions related to the UDALOY-II

The final proposition identifies two platforms (doublet), with one of them being correct.Since the lists of emitters on these two platforms are identical except for one emitter (emitter#63 TOP-PLATE, which is on the UDALOY-SPIRIDONOV), we can explain the finalresult by the fact that the random selection of the emitter among the list did not select thediscriminating emitter during the life duration of proposition 4. As described before, Figure14 shows a set of triangles which corresponds to the reception of an ESM contact. Three ofthem have been coloured since they play a key role in the evolution of the propositions. The


left-most coloured triangle corresponds to emitter #69 ( BELL-SQUAT ), which onlybelongs to the three versions of the UDALOY. The consequence is a drop of the mass ofproposition 2, leaving behind the MODIFIED-KIEV. The second interesting ESM contactcorresponds to emitter #63 ( TOP-PLATE ), which only belongs to the UDALOY-II and theUDALOY-SPIRIDONOV. The consequence is a drop of the mass associated withproposition 3, leaving behind the UDALOY-AND-KULAKOV. The third interesting ESMcontact (right-most coloured triangle) corresponds again to emitter #63. The consequence isan increase of the mass associated with proposition 4. Figure 15 (Lockheed, 1998) representsthe successive propositions of highest mass in the successive declarations on a scale differentfrom Figure 14. However, its particular shape is entirely explained by Figure 14.

432

Figure 15. Temporal evolution of the proposition of highest mass in the UDALOY II case

ID estimation of the platform KARA-AZOV

Figure 16 shows the temporal evolution of the mass associated with four typical propositionsrelated to the KARA-AZOV (Lockheed, 1998). These propositions are identified by thenumbers 1, 2, 3, 4 in the following table.


Table 5. Propositions appearing for the cruiser KARA-AZOV


1 IVAN-ROGOV, IVAN-ROGOV-ALEKSANDR, IVAN-ROGOV-MITROFAN,

KARA-KERCH, KARA-AZOV, KARA-PETROPAVLOSK, KARA-VLADIVOSTOK

2 KARA-KERCH, KARA-AZOV, KARA-PETROPAVLOSK, KARA-VLADIVOSTOK

3 KARA-KERCH, KARA-AZOV, KARA-VLADIVOSTOK

4 KARA-AZOV

12 3

42 = Kara quartet only

3 = Kara triplet

4 = Kara-Azov

1 = 7 platforms incl. Kara quartet

#92

Figure 16. Temporal evolution of typical propositions related to the KARA-AZOV

The final proposition identifies the correct platform (singleton). The selected ESM contact(coloured triangle) corresponds to emitter #92 ( TOP-DOME ), which only belongs to theKARA-AZOV. This time, the random selection of the emitter helped us by selecting thediscriminating one during the life duration of proposition 3 to decrease its associated mass and


increase the mass associated with the final singleton. Figure 17 (Lockheed, 1998) representsthe successive propositions of highest mass in the successive declarations on a scale differentfrom Figure 16. However, its particular shape is entirely explained by Figure 16.

432

Figure 17. Temporal evolution of the proposition of highest mass in the KARA-AZOV case

ID of Russian ships with the SSAR ISMID estimation of the platform MIRKA-II

Figure 18 shows the temporal evolution of the mass associated with three typical propositionsrelated to the platform MIRKA-II (Lockheed, 1998). These propositions are identified by thenumbers 1, 2, 3 in the following table.

Table 6. Propositions appearing for the frigate MIRKA-II


1 GRISHA-III, MIRKA-I, MIRKA-II, KARA-KERCH, SAM-KOTLIN, MOSKVA, KARA-PETROPAVLOSK, KARA-VLADIVOSTOK, UGRA-II

2 GRISHA-III, MIRKA-II, UGRA-II

3 MIRKA-II


1

2 3

1 = 9 platformsincl. Mirka doublet

2 = Mirka II, Grisha III, Ugra II

3 = Mirka II

#109 #55#56

SAR data

Figure 18. Temporal evolution of typical propositions related to the MIRKA-II with SSAR

The final proposition identifies the correct platform (singleton). As described before, Figure18 shows a set of triangles corresponding to the reception of an ESM contact. Three of themhave been coloured since they play a key role in the evolution of the propositions. The left-most coloured triangle corresponds to emitter #109 ( SQUARE-HEAD ), which only belongsto the GRISHA-III, MIRKA-II and MIRKA-I. The consequence is a drop of the mass ofproposition 1, leaving behind the other platforms. The second interesting ESM contactcorresponds to emitter #56 ( HAWK-SCREECH ), which only belongs to the MIRKA-II.The consequence is a drop of the mass associated with proposition 2, leaving behind theGRISHA-III and UGRA-II. The third interesting ESM contact (right-most coloured triangle)corresponds to emitter #55 ( SLIM-NET ), which does not belong to the GRISHA-III orUGRA-II, explaining the drop of proposition 2 and the increase of proposition 3.

A fourth coloured triangle has been drawn on top of the others. It corresponds to the time atwhich the propositions (Ship Length, Ship Type, Ship Category) associated with the SARacquisition are fused with the others. It corresponds to 1980 seconds after the start of thescenario. Since just a few seconds before (s 1950), a favourable emitter (#56) had beendetected, Figure 18 does not show if the SAR attributes contributed in increasing the mass ofthe correct proposition. Figure 19 (Lockheed, 1998) represents the successive propositions of


highest mass in the successive declarations on a scale different from Figure 18. However, itsparticular shape is entirely explained by Figure 18.

2 3

Figure 19. Temporal evolution of the proposition of highest mass in the MIRKA-II case with SSAR


Figure 20 shows the temporal evolution of the mass associated with three typical propositionsrelated to the UDALOY-II (Lockheed, 1998). These propositions are identified by thenumbers 1, 2, 3 in the following table.

Table 7. Propositions appearing for the destroyer UDALOY-II


1 KARA-KERCH, KARA-VLADIVOSTOK

2 UDALOY-II, UDALOY-AND-KULAKOV, UDALOY-SPIRIDONOV

3 UDALOY-II, UDALOY-SPIRIDONOV


1

23

1 = wrong Kara doubletdue to false emitter

2 = Udaloy triplet

3 = Udaloy doubletcontaining the Udaloy II

#69 #63

SAR data

Figure 20. Temporal evolution of typical propositions related to the UDALOY-II with SSAR

The final proposition identifies the two platforms (doublet) UDALOY-II and UDALOY-SPIRIDONOV. As described before, Figure 20 shows a set of triangles corresponding to thereception of an ESM contact. Two of them have been coloured since they play a key role inthe evolution of the propositions. The very first (left-most coloured triangle) corresponds toemitter # 69 ( BELL-SQUAT ), which only belongs to the platforms of the familyUDALOY. The consequence is the emergence of proposition 2 and the abrupt drop of themass of proposition 1. The second interesting ESM contact corresponds to emitter #63

TOP-PLATE ), which only belongs to the UDALOY-II and UDALOY-SPIRIDONOV.The consequence is a drop of the mass associated with proposition 2, leaving behind theUDALOY-AND-SPIRIDONOV.

A third coloured triangle has been drawn on top of the others. It corresponds to the time atwhich the propositions (Ship Length, Ship Type, Ship Category) associated with the SARacquisition are fused with the others. It corresponds to 1980 seconds after the start of thescenario. Since just a few seconds before (s 1946), a favourable emitter (#69) had beendetected, a little bump is noticeable at this time. As for the MIRKA-II, Figure 20 does notshow if the SAR attributes contributed in increasing the mass of the correct proposition.Figure 21 (Lockheed, 1998) represents the successive propositions of highest mass in the


successive declarations on a scale different from Figure 20. However, its particular shape isentirely explained by Figure 20.

2 1 3

Figure 21. Temporal evolution of the propositions of highest mass in the UDALOY-II case withSSAR


Figure 22 shows the temporal evolution of the mass associated with three typical propositionsrelated to the KARA-AZOV (Lockheed, 1998). These propositions are identified by thenumbers 1, 2, 3 in the following table.

Table 8. Propositions appearing for the cruiser KARA-AZOV


1 KARA-KERCH, KARA-AZOV, KARA-PETROPAVLOSK, KARA-VLADIVOSTOK

2 KARA-KERCH, KARA-AZOV, KARA-VLADIVOSTOK

3 KARA-AZOV


1

2

3

2 = Kara triplet

= Kara-Azov

1 = Kara quartet

#92 #93

SAR data

Figure 22. Temporal evolution of typical propositions for the KARA-AZOV with SSAR

The final proposition identifies the correct platform. As described before, Figure 22 shows aset of triangles corresponding to the reception of an ESM contact. Three of them have beencoloured since they play a key role in the evolution of the propositions. The left-mostcoloured triangle corresponds to emitter # 92 ( TOP-DOME ), which only belongs to theplatform KARA-AZOV. The consequence is the emergence of proposition 3 and the abruptdrop of the mass of proposition 1. The second interesting ESM contact corresponds to theemitter #93 ( FLY-SCREEN ), which belongs to the KARA-KERCH, KARA-AZOV andKARA-VLADIVOSTOK, but not to the KARA-PETROPAVLOSK. The consequence is adrop of the mass associated with proposition 1 and the emergence of proposition 2.

A third coloured triangle has been drawn on top of the others. It corresponds to the time atwhich the propositions (Ship Length, Ship Type, Ship Category) associated with the SARacquisition are fused with the others. It corresponds to 1980 seconds after the start of thescenario. Unfortunately, its impact cannot be seen as an increase of the mass of proposition 3.Figure 23 (Lockheed, 1998) represents the successive propositions of highest mass in thesuccessive declarations on a scale different from Figure 22. However, its particular shape isentirely explained by Figure 22.


1 3

Figure 23. Temporal evolution of the proposition of highest mass in the KARA-AZOV case withSSAR

ID of Russian Ships with few ESM reportsID estimation of the platform MIRKA-II

Figure 24 shows the temporal evolution of four propositions having the highest mass(Lockheed, 1999) for a certain period of time. Five triangles at the bottom of the figurerepresent the time at which an ESM report was fused. The chronology of significant events isas follows:

a. After the first 10 minutes (t = 656 s), the KARA-AZOV and the MIRKA arenot properly resolved (within an angle of 1º). The emitter #92, belonging tothe KARA-AZOV and other platforms, is detected, provoking the initiation ofproposition P1 in which the MIRKA-II is absent.

b. Then, at t = 1293 s, the emitter #103 is detected, which belongs to theMIRKA-II and the KARA-AZOV. As a result, proposition P2 emerges. Theground-truth shows that it is emitted this time by the MIRKA-II.


c. At t = 1950 s, the emitter #56 is detected, which only belongs to the MIRKA-II. The mass associated with proposition P2 decreases.

d. An SAR image is acquired and analyzed at time t = 1980 s. The fusion of theShip Length attribute confirms the elimination of proposition P2 (the KARAis a cruiser twice the length of the MIRKA-II), and proposition P3 becomespre-eminent. The fusion of the Ship Type attribute at time t = 2040 sincreases the mass of proposition P3. The masses associated with thesedeclarations were discussed above in section 5.1.1.

e. Finally, at time t = 2606 s and 3243 s, two emitters (#44, #55) belonging onlyto the MIRKA-II trigger the emergence and confirmation of proposition P4.

Ship Type

Ship Length

Proposition P1:

Kirov-UshakovKirov-LazarevKirov-VelikyiKara-Azov

Prop. P4:

Mirka-II

92 103 56 44 55

Mirka-II’sEmitter List

44, 47, 55,56,103,109 Proposition P2:

Kara-Azov

Proposition P3:

Mirka-IMirka-IISam-Kotlin

Figure 24. Temporal evolution of typical propositions of high mass for the MIRKA-II


Figure 25 below shows (Lockheed, 1999), for the KARA-AZOV, the same type ofinformation as shown in Figure 24. The chronology of significant events is as follows:


a. At time t = 1275 s, emitter #45 is detected, which belongs to a large list ofplatforms, creating proposition P1.

b. Then at time t = 1950 s, emitter #104, belonging only to ships of the Karaclass, is detected. The mass associated with proposition P1 decreases andproposition P2 is initiated.

c. An SAR image is acquired and analyzed at time t = 1980 s. The fusion of theShip Length attribute eliminates proposition P1 and the mass associated withproposition P2 increases. The fusion of the Ship Type attribute (t = 2040 s)contributes to the increase in the mass of proposition P2.

d. Finally, at time t = 2606 s, 3225 s and 3243 s, three emitters (#68, #64, #78)belonging only to ships of the Kara class are successively fused, increasingthe mass of proposition P2. Since the emitter belonging specifically to theKARA-AZIV (emitter #92) has not been detected and all detected emittersare common to all ships of the Kara class, the final proposition identifies allthe ships of the Kara class known in our PDB.

Ship Length

Ship Type

45 104 68 64, 78

Proposition P1:

Grisha-IIIKrivak-IA/IBKrivak-IIIA/IIIBKara-KerchKirov-UshakovKirov-NakhimovKirov-LazarevKirov-VelikyiKara-AzovKara-PetropavloskKara-VladivostokIvan-Rogov-Aleks.Ivan-Rogov-Mitro.

Proposition P2:

Kara-AzovKara-PetropavloskKara-Vladivostok Kara-Azov’s

Emitter List

45, 46, 62, 64, 68, 78, 84, 85, 92, 93, 103, 104

Figure 25. Temporal evolution of typical propositions of high mass for the KARA-AZOV



Figure 26 shows (Lockheed, 1999), for the UDALOY-II, the same type of information asshown in Figure 24. The chronology of significant events is as follows:

a. At time t = 637 s, the emitter #97 is detected, which belongs to the ships ofthe Udaloy class and to the modified-Kiev, initiating proposition P1.

b. Then, at time t = 1275 s, emitter #129 is fused. It does not normally belong tothe UDALOY-II but it has been placed intentionally in her list of emitters tosimulate countermeasures (hence the report is denoted by CM). It actuallyworked, since the mass associated with proposition P1 dropped andproposition P2 carrying a false identity was initiated.

c. An SAR image is acquired and analyzed at time t = 1980 s. The fusion of theShip Length attribute on two propositions has the effect of decreasing themass associated with proposition P2 while creating proposition P3 byretaining from proposition P1 the ships of class Udaloy. The fusion of theShip Type helps in decreasing the mass associated with the false identity(proposition P2).

d. At time t = 2606 s, emitter #71 is detected, which unfortunately will not helpin discarding proposition P2, since this emitter belongs to the ships of classesUdaloy and Sovremenny.

e. Finally, at time t = 3243 s, the final correct decision is made when emitter#93 belonging only to the ships of the class Udaloy is detected and fused.


Udaloy-II’sEmitter List in PDB

63, 65, 69, 71, 91, 93,

97, 128, 131

Used Emitter List

63, 65, 69, 71, 77, 91,

93, 97, 129

Ship Length

Ship TypeInitial Proposition:

Modified-KievUdaloy-IIUdaloy-KulakovUdaloy-Spiridonov

Final Proposition:

Udaloy-IIUdaloy-KulakovUdaloy-Spiridonov

Intermed. Prop.:

Sovremenny-IISovremenny-OsmotriteSovremenny-Boyevoy

97 129 71 93

CM

Figure 26. Temporal evolution of typical propositions of high mass for the UDALOY-II

ID of Russian ships in electro-magnetically silent scenarioThe platform ID results shown in the previous section fused a moderate amount of ESMreports with a single (but complex) ISM interpretation of one SSAR image. The scenariochosen had to assume that the image was taken when the Aurora was side-looking at thetargets heading on a relative bearing of 45 degrees. However, since only the relativeorientation of the platforms is relevant when acquiring an SAR image, the ISM report couldvery well have occurred earlier in the scenario if, for example, the Russian ships had beenheading N (instead of NE) and the Aurora had taken the image earlier when 141 km away(such that the relative orientation is the same and the resolution has not degraded much). TheISM would then have played a more prominent role in platform ID, since fewer ESM reportswould have been fused by that time. To show that the ISM classifier results by themselveslead to a useful ship identification, this section has the ESM turned off, as if it weremalfunctioning or the platforms wished to remain electro-magnetically silent. All the resultspresented in this section are from reference (Lockheed, 1999).


ID estimation of the platform MIRKA-II

Table 9 shows the evolution of the generic propositions that result from the fusion of thelength and the ship category and type declarations from the ISM SSAR classifier Steps 2 and3 for the Mirka platform. The first line indicates the presence of the MIRKA-II in thebitstream of the PDB, the second group of propositions indicates the result after fusion of thelength, and the third group indicates the result after fusion of ship category and type.

Table 9. SSAR ISM declarations and resulting generic identification for the MIRKA-II

MIRKA.2 00000000 00000000 00100000 00000000 00000000 00000000 00000000 00000000 00000000 00000000

LENGTH

1980 s MASS

? 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 0.02

LGTH_1 00100000 11101100 01100000 00010000 00000000 00000000 00000000 00000100 00000001 10111111 0.98

LENGTH + SHIP CATEGORY + SHIP TYPE

2040 s MASS

? 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 0.010

L1+FRIG 00100000 11101100 01100000 00000000 00000000 00000000 00000000 00000000 00000000 10000000 0.879LGTH_1 00100000 11101100 01100000 00010000 00000000 00000000 00000000 00000100 00000001 10111111 0.091

FRIGATE 01101100 11101100 01111111 01001000 00000000 00000000 00000000 00000000 00000110 10000000 0.017MERCH 10000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 0.001DESTR 00000001 00000000 00000000 10000000 01000010 00000000 00000000 00010001 11111000 00000000 0.000

CRUISR 00010010 00000000 10000000 00000010 10111000 00111110 00000000 00001000 00000000 00000000 0.000SURMIL 01111111 11111111 11111111 11111111 11111111 10111111 11110111 11111011 11111110 10000000 0.000LGTH_1 00100000 11101100 01100000 00010000 00000000 00000000 00000000 00000100 00000001 10111111 0.000

Figure 27 below shows the temporal evolution of the mass of typical propositions without anyESM reports. The first message from the ISM is associated with step 2 of the SAR classifierand corresponds to a minimum length Length_1 declaration. Note that length measurementsare binned in 40 m intervals according to section 2.4 and that the Mirka was declared anunusually small frigate, a type which is already the smallest line combatant.

This is followed in short order by the declaration consisting of the proposition: Frigate 86%,along with a very small merchant declaration as shown in the table above. The resultingfusion initiates the correct proposition Length_1+Frigate having the largest mass (solidline), the dash-dotted line corresponding to Length_1 and the dashed line to Frigate only.This demonstrates that the ISM alone can provide an adequate (if more complex) platformidentification in an electro-magnetically silent environment.


LGTH_1

LGTH_1 +FRIGATE

LGTH_1FRIGATE

MIRKA-2FRIGATE

Figure 27. Time evolution of the generic identification of the MIRKA-II from the SAR ISM only


Table 10 shows the evolution of the generic propositions that result from the fusion of thelength and the ship category and type declarations from the ISM SAR classifier Steps 2 and 3for the Udaloy platform. The first line indicates the presence of the UDALOY-II in thebitstream of the PDB, the second group of propositions indicates the result after fusion of thelength, and the third group indicates the result after fusion of ship category and type.


Table 10 SAR ISM declarations and resulting generic identification for the UDALOY-II

UDAL.2 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000001 00000000 00000000

LENGTH

1980 s MASS

? . 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 0.02LGTH_2 01001101 00000000 00011111 11001100 01010000 00000001 10000000 00000010 01101110 00000000 0.47LGTH_3 00010010 00000000 10000000 00100010 00101010 00001110 00000000 00011001 10010000 01000000 0.47

LENGTH + SHIP CATEGORY + SHIP TYPE

2040 s MASS

? 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 0.011L2+DES 00000001 00000000 00000000 10000000 01000000 00000000 00000000 00000000 01101000 00000000 0.260L3+DES 00000000 00000000 00000000 00000000 00000010 00000000 00000000 00010001 10010000 00000000 0.260L2+CRU 00000000 00000000 00000000 00000000 00010000 00000000 00000000 00000000 00000000 00000000 0.157L3+CRU 00010010 00000000 10000000 00000010 00101000 00001110 00000000 00001000 00000000 00000000 0.157LGTH_2 01001101 00000000 00011111 11001100 01010000 00000001 10000000 00000010 01101110 00000000 0.048LGTH_3 00010010 00000000 10000000 00100010 00101010 00001110 00000000 00011001 10010000 01000000 0.048L2+FRIG 01001100 00000000 00011111 01001000 00000000 00000000 00000000 00000000 00000110 00000000 0.043DESTR 00000001 00000000 00000000 10000000 01000010 00000000 00000000 00010001 11111000 00000000 0.010

Figure 28 below shows the temporal evolution of the mass of typical propositions without anyESM reports. The first message from the ISM is associated with Step 2 of the SAR classifierand corresponds to two contiguous length interval propositions in the 40 m binning scheme ofsection 2.4 (the small dashed line of mass 0.49 corresponds to both Length_2 andLength_3 ).

This is followed in short order by the declaration consisting of the three non-null propositionsshown in Section 6, namely: Destroyer 48%, Cruiser 29%, and Frigate 4%. The resultingfusion initiates the correct proposition Length_2+Destroyer together withLength_3+Destroyer having the largest mass (solid line), the dash-dotted line corresponding

to Length_2+Cruiser together with Length_3+Cruiser and the dashed line to Length_2together with Length_3 . This demonstrates again that the ISM alone can provide anadequate (if more complex) platform identification in an electro-magnetically silentenvironment.


LGTH_2LGTH_3

LGTH_2LGTH_3

LGTH_2 + DESTLGTH_3 + DEST

LGTH_2 + CRULGTH_3 + CRU

UDALOY-2DESTROYER

Figure 28. Time evolution of generic identification of UDALOY-II from the SSAR ISM only


Table 11 shows the evolution of the generic propositions that result from the fusion of thelength and the ship category and type declarations from the ISM SSAR classifier Steps 2 and3 for the Kara platform. The first line indicates the presence of the Udaloy-II in the bitstreamof the PDB, the second group of propositions indicates the result after fusion of the length,and the third group indicates the result after fusion of ship category and type.


Table 11. SAR ISM declarations and resulting generic identification for the KARA-AZOV

KARA-AZ 00000000 00000000 00000000 00000000 00000000 00001000 00000000 00000000 00000000 00000000

LENGTH

1980 s MASSθ 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 0.02

LGTH_3 00010010 00000000 10000000 00100010 00101010 00001110 00000000 00011001 10010000 01000000 0.49LGTH_4 00000000 00010011 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 0.49

LENGTH + SHIP-CATEGORY + SHIP-TYPE

2040 s MASSθ 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 11111111 0.010

L3+CRU 00010010 00000000 10000000 00000010 00101000 00001110 00000000 00001000 00000000 00000000 0.596L4+CAR 00000000 00010011 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 0.153LGTH_3 00010010 00000000 10000000 00100010 00101010 00001110 00000000 00011001 10010000 01000000 0.117L3+DEST 00000000 00000000 00000000 00000000 00000010 00000000 00000000 00010001 10010000 00000000 0.090CRUISR 00010010 00000000 10000000 00000010 10111000 00111110 00000000 00001000 00000000 00000000 0.024DESTR 00000001 00000000 00000000 10000000 01000010 00000000 00000000 00010001 11111000 00000000 0.003MERCH 10000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 00000000 0.002CARRI. 00000000 00010011 00000000 00000001 00000001 00000000 00000111 11100000 00000000 00000000 0.001

Figure 29 below shows the temporal evolution of the mass of typical propositions without anyESM reports. The first message from the ISM is associated with Step 2 of the SAR classifier,and corresponds to two contiguous length interval propositions (the small dashed line of mass0.49 corresponds to both Length_3 and Length_4 in the 40 m binning scheme of section2.4).

This is followed in short order by the declaration consisting of the three non-null propositionsshown in Section 6, namely: Destroyer 10%, Cruiser 67%, and Aircraft Carrier 4%. Theresulting fusion initiates the correct proposition Length_3+Cruiser having the largest mass(solid line), the dash-dotted line corresponding to Length_4+Carrier and the dashed line toLength_3 only. This demonstrates again that the ISM alone can provide an adequate (if

more complex) platform identification in an electro-magnetically silent environment.


LGTH_3 + CRUISER

LGTH_4 + CARRIER

LGTH_3

LGTH_3LGTH_4

KARA-AZOVCRUISER

Figure 29.. Time evolution of generic identification of KARA-AZOV from the SAR ISM only

Conclusions for the MAAO scenarioThe study of the Maritime Air Area Operation scenario leads us to the following remarks:

a. The ESM reports play a prominent role in identification, since one deals withplatforms of the same type moving at the same speed. Note that this may be avery common situation for the Aurora.

b. DFDM-3 succeeds in identifying platforms even when they are employingcountermeasures.

c. Finally, the above results can vary a great deal due to the random selection ofthe emitters from the emitter name list (ENL).

The Maritime Air Area Operation scenario will have to be re-examined under more realisticconditions:

a. Using a more realistic ESM simulator, since this sensor plays a major role.

b. Evaluating the internal normalization of the masses of the propositionsperformed in DS to keep the mass of the ignorance above a given threshold.


In some situations, this may make an incoming favourable propositionundiscernible.

5.2 DFS

5.2.1 ISM resultsThe DFS scenario contains the imagery of four American ships under the same conditions(and with the same caveats) as the first scenario.

Figure 30 shows (Lockheed, 1998) the raw SAR imagery in reverse video and histogramequalized (on top), the segmented image with its extracted centreline by the Hough transform,and the thresholded major scatterers for the American ships COONTZ, TICONDEROGA andVIRGINIA.

Figure 30. SAR images for the American ships COONTZ, TICONDEROGA and VIRGINIA

Note that the SAR acquisition parameters are:

a. aircraft altitude: 7.62 km





e. range resolution: 0.75 m

f. cross-range resolution: 2.0 m.

For each of the three imaged ships, the ISM s hierarchical classifier again generates threeattributes in succession, each of which leads to several identity declarations (with associatedmasses in the Dempster-Shafer sense) for Line ships:

a. first the length (obtained after centreline detection);

b. next the Line category with its confidence level (obtained by keeping the top10% of the strongest pixels);

c. finally the Line type (from a choice of five Line types: frigate, destroyer,cruiser, battleship or aircraft carrier).

For the destroyer COONTZ (Lockheed, 1998):


b. The masses for the ship category declaration are: Line 86%; Merchant 5%;Unknown 9%.

c. Finally the masses for Line ship type are: Frigate 11%; Destroyer 53%;Cruiser 21%; Battleship 0%; Aircraft Carrier 0%.

Note that the ISM correctly identifies the COONTZ as a destroyer.

For the cruiser TICONDEROGA (Lockheed, 1998):


b. The masses for the ship category declaration are: Line 83%; Merchant 4%,Unknown 13%.

c. Finally the masses for Line ship type are: Frigate 4%; Destroyer 36%; Cruiser41%; Battleship 0%; Aircraft Carrier 1%.

Note that the ISM correctly identifies the TICONDEROGA as a cruiser.

For the cruiser VIRGINIA (Lockheed, 1998):



b. The masses for the ship category declaration differ only very slightlydepending on whether one uses the centreline or the range profile: Line 76%;Merchant 3%; Unknown 20%.

c. Finally the masses for Line ship type are: Frigate 25%; Destroyer 43%;Cruiser 8%; Battleship 0%; Aircraft Carrier 0%.

Note that the ISM incorrectly identifies the VIRGINIA as a destroyer because of its relativelysmall size. However, the non-null value for the cruiser proposition is enough for theevidential reasoning scheme.

It should also be noted that in this case the masses for the ship category declaration differ byless than 2% whether one uses the centreline or the range profile decompositions (an averagevalue is given in the text).

5.2.2 Identification of the American fleet through ESM and SAR ISMIn the DFS scenario, the American fleet is far enough away to be imaged by the SAR butgeometrical considerations have degraded the ESM s contribution to the ID of the Americanfleet. Indeed, this time, the typical angular separation between ships can be as little as 0.5 kmat 100 km or 0.005 radians, which is quite comparable to the intrinsic bearing accuracy of theradar. In other words, even after Kalman filtering, the tracks are expected to be angularlyvery close and the changing aspects of the American fleet (heading at 45 degrees), as viewedby the CP-140, can cause several crossings in bearing between the tracks corresponding toeach American ship. One thus expects a greater number of false associations of ESM to trackthan in the Canadian fleet case, due to the combined effect of high (classified) ESM bearingaccuracy and intrinsic filtered radar track accuracy. It is hoped that SAR imaging can helpresolve the resulting ID ambiguity and provide an ultimately correct ID. This is therefore astringent test of stable filtering and association, in addition to the evidential reasoning testedin the MAAO scenario.

In the DFS scenario, with SAR imaging of the American fleet (at the same distance and aspectangles as in the MAAO scenario), one obtains the results for the ID of the American fleetshown in the following figures. In order to follow the ID evolution in the followingsubsections, Table 12 shows the emitter list for the American fleet. Note that only the firstthree are imaged, but all ships are so closely separated that their emitters are occasionallyassociated with other ships depending on the line-of-sight and the ESM bearing accuracy used(representative of a classified number).

Again, the emitters carried by the platforms have many common elements and emitters areselected at random for a given platform, from run to run. It is reasonable to assume that,during the course of a simulation, most, if not all, emitters will have been selected,representing the fact that ships use all of their radar during a given mission.


Table 12. Emitter list for the American fleet

Ship name List of emittersCOONTZ 7 8 13 16 18 33 34 35 57

TICONDEROGA 7 8 13 32 53 54 57 110 112

VIRGINIA 7 8 13 15 16 31 32 53 54 57

SPRUANCE 8 14 18 31 32 43 53 57 114 115 119 121

SACRAMENTO 7 13 18 33 42 121 130

NIMITZ 7 8 16 17 54 57 115 117 121 122 124 125 126 127

ID estimation of the destroyer COONTZ

Figure 31 (Lockheed, 1999) shows that the ESM reports already prefer the destroyerCOONTZ identification, since emitter #16 was identified (common also on the VIRGINIA),and that the Ship Length (SL) (since the COONTZ is smaller than the VIRGINIA) and, later,the Ship Category (SC) and Ship Type (ST) only reaffirm this correct ID, despite emitter #15(not on the COONTZ) having been fused at similar times. One can see that the threedeclarations from the SSAR overcome the single incorrect ESM declaration.

31 16 7 15 31 16 31 13 34

SL-SC+ST

COONTZ

Figure 31. ID time evolution for the COONTZ


ID estimation of the cruiser TICONDEROGA

The situation for the TICONDEROGA is slightly more complicated. Figure 32 (Lockheed,1999) shows that, at first, due to association of the closely spaced American fleet at such agreat distance (relative to the unclassified sensor accuracy used), both the VIRGINIA and theTICONDEROGA are possible. The simultaneous fusion of emitter #110 (solely on theTICONDEROGA), without the help of the SL and SC+ST ISM declarations, resolves theambiguous ID.

32 53 57 32 32 7 110 112 32 112 54 110

SL-SC+ST

TICONDEROGA

VIRGINIATICONDEROGA

Figure 32. ID time evolution for the TICONDEROGA

ID estimation of the cruiser VIRGINIA

As for the cruiser VIRGINIA, (which is incorrectly identified by the SAR ISM as a destroyerlike the SPRUANCE), Figure 33 (Lockheed, 1999) shows that emitter #13 (present on theVIRGINIA but not the SPRUANCE) decreases the belief in the SPRUANCE and increasesthe belief in the VIRGINIA. However, after fusion of emitter #18 (present on theSPRUANCE but not the VIRGINIA) and the incorrect ISM declaration, rapidly followed by


yet another emitter #18 declaration, the final identification favours the SPRUANCE until oneis well past the American fleet. In this case, the closeness of the two platforms has caused toomany false ESM declarations to be associated with the VIRGINIA to reverse the incorrectISM declaration.

33 18 13 18 13 18 130 130 121 18

SL-SC+ST

VIRGINIA

SPRUANCE

SPRUANCE +SPRUANCE-HAYLER

VIRGINIA +SPRUANCE-HAYLER

Figure 33. ID time evolution for the VIRGINIA

5.2.3 Identification of Canadian fleet at short range through ESMIn the DFS scenario, the Canadian fleet is too close for SAR imaging, so the identificationmust rely mostly on ESM fusion. It should be emphasized that the tracks for each of theCanadian ships should have an angular spread which is better, after Kalman filtering, than thebearing accuracy of the radar, namely better than 0.24 degrees or 0.004 radians. When theCP-140 is broadside to the Canadian fleet, the typical separation between the ships is 0.5 kmat 20 km, which translates into an angular separation between ships of 0.025 radians. In otherwords, the ships should be well separated in the dynamic track database, even though theships can sometimes be in direct line-of-sight (LOS) of each other, e.g., when broadside of theCP-140, the HALIFAX and the IMPROVED RESTIGOUCHE are in LOS configuration. TheESM s bearing accuracy is classified, but one can see that the tracks have well separatedbearings and that mis-association due to LOS or an occasional large bearing drift from an


ESM report can be the only causes for possible bad ID being fused from the ESM. This is notexpected to occur often.

The Canadian fleet is composed of the support ship IMPROVED PROVIDER behind a trio ofline ships. As the CP-140 approaches to eventually become broadside at roughly 1 hour(3600 seconds), the frigate HALIFAX and destroyer IROQUOIS stay at first in near LOS. Asthe CP-140 comes broadside, the frigates HALIFAX and IMPROVED RESTIGOUCHE arein LOS. One should therefore expect that ESM returns from the HALIFAX and IROQUOISare indistinguishable until one gets close to one hour in the scenario. The RESTIGOUCHEand PROVIDER should be easily identified first, as they are well separated in bearing earlyon in the scenario. As the CP-140 comes broadside, since the RESTIGOUCHE is alreadyidentified, very little confusion with the HALIFAX is expected in the evidential reasoningscheme. The following subsections prove this point.

In order to follow the performance of the association mechanism and the truncated Dempster-Shafer evidential reasoning scheme, the emitter list for the Canadian fleet is reproducedbelow.

Table 13 Emitter list for the Canadian fleet

Ship name List of emittersIMPROVED PROVIDER 8 42 43 75 76

HALIFAX 6 7 8 57 58 59 60 61

IMPROVED RESTIGOUCHE 7 33 36 37 38 39 58

IROQUOIS 7 8 23 36 59 72

Another Canadian ship, the BREMEN, shares emitter # 59 with both the HALIFAX and theIROQUOIS and emitter # 23 with the IROQUOIS.

All figures in this section for the Canadian fleet come from reference (Lockheed, 1999).


ID estimation of the frigate HALIFAX

HALIFAX

HALIFAX+

IROQUOISHALIFAX+

BREMEN+

IROQUOIS

59 59 61 6 59 60 58

Figure 34 ID time evolution for the Frigate HALIFAX

Figure 34 shows, as expected, that since the HALIFAX and IROQUOIS are in LOS at first,they are indistinguishable until they come broadside after about one hour.


ID estimation of the destroyer IROQUOIS

IROQUOIS

HALIFAX+

BREMEN+

IROQUOIS

BREMEN+

IROQUOIS

59 23 59 72 23 36 23

Figure 35 ID time evolution for the Destroyer IROQUOIS

Figure 35 shows, as expected, that since the HALIFAX AND IROQUOIS are in LOS at first,they are indistinguishable until they come broadside after about one hour. The BREMENoccurs again because of two emitters in common with the IROQUOIS.


ID estimation of the frigate IMPROVED RESTIGOUCHE

RESTIGOUCHE

RESTIGOUCHE+

IROQUOIS

36 36 37 36 58 58 58 36 38 36 33 38 36

Figure 36 ID time Evolution for the Frigate IMPROVED RESTIGOUCHE

Figure 36 shows that the IMPROVED RESTIGOUCHE is identified very early in thescenario, as expected.


ID estimation of the support ship IMPROVED PROVIDER

PROVIDER

ST-LAURENT+

VIRGINIA+

PROVIDER

75 42 76 75 43 76 43 75 75 76 42 76

Figure 37 ID time evolution for the Support Ship IMPROVED PROVIDER

Figure 37 shows that the IMPROVED PROVIDER is identified very early in the scenario, asexpected.

5.2.4 Conclusions for the DFS scenarioCorrect final ID is achieved in all cases where the targets are sufficiently well separated andmost of the time in highly dense environments. Correct ID is therefore always obtained forthe Canadian fleet, but only sometimes for the American fleet, whose geometric dispositionwas conceived such that ESM associations were difficult for a single-scan mechanism. Itwould be advisable to upgrade the association mechanism in that case to multi-scanalgorithms such as the multi-hypotheses tracker (MHT) or the multi-frame association (MFA).Since highly dense environments are not the norm, a context-sensitive mechanism should beimplemented on the KBS, to preserve single-scan associations in the general case, and theswitch to multi-scan should be made only when warranted by a dense environment.


5.3 Other scenarios

5.3.1 ISM results for the counter-drug operations scenarioThe third scenario contains imagery of only one ship, the QUEST, for which the acquisitionparameters are:

a. aircraft altitude: 1.5 km (5000 feet)




e. range resolution: 0.75 m

f. cross-range resolution: 2.0 m

g. ship heading: 45 degrees.

Figure 38 (Lockheed, 1998) shows the raw SAR imagery in reverse video and histogramequalized (on top), the segmented image with its extracted centreline by the Hough transform,and the thresholded major scatterers for the QUEST.

Figure 38 SAR image of the vessel QUEST


The centreline detection leads to a calculation of an apparent target length of 76 m for aninterval of ship length declaration of 68 - 108 m. This rather small ship length indicateseither:

a. a small ship

b. a front view

c. a bad segmentation

d. a bad ship end-points detection.

This usually means that the ensuing ship identity declaration will not be reliable.

A 10% percentage of the strongest pixels is kept, resulting in the following masses for shipcategory: Line 0.86; Merchant 0.5; Unknown 0.09. This declaration is incorrect but notunexpected, due to the low eccentricity mentioned above. If the MSDF system is viewed asan operator s decision aid tool, the operator should be notified of the dubious length attributedetermination, and he/she should veto the category and type declarations. Having done so,since the QUEST is alone according to the scenario, and it has only one characteristic emitter,#79, according to the PDB in the first report of this series, it will be identified as soon as ituses its radar.

5.3.2 Maritime sovereignty patrol scenarioThis scenario would have necessitated real imagery, since none of the ships susceptible to beimaged, such as fishing boats ranging in size from commercial to pleasure boats, trawlers,etc., can serve as input to the SAR SIM imagery generator. It therefore has not been studiedfurther.


6. Conclusions and suggestions for future researchThis report demonstrated the achieved performance of judiciously selected data/informationfusion and object recognition algorithms for realistic sensor simulations in relevant airbornemaritime surveillance missions. Given the scenarios examined, a single-scan associator suchas nearest-neighbour or a Jonker-Volgenant-Castanon variant of it, is chosen and a singleKalman filter track update mechanism is performed for the positional fusion component.Identity is obtained through an identification fusion done by a truncated DS algorithm withnear-optimal truncation parameters, and a minimum for the ignorance, which ensuresrecovery under countermeasures.

The test bed architecture is based on a KBS developed by LM Canada in collaboration withDRDC Valcartier. The chosen MSDF and ISM algorithms are agents on this data-drivenKBS. A complete design of both the MSDF and the ISM is presented, with all types ofobjects clearly identified, namely: data types, algorithmic agents and context functions. Inparticular, the functional components of the MSDF are clearly delineated into registration (oralignment), association, state (or position) update and identity update components, as per theprevious reports. Similarly, the ISM components are detailed and automatic vs. operator-controlled versions are highlighted.

Complete results were presented for

• Maritime Air Area Operations (MAAO) and

• Direct Fleet Support (DFS).

scenarios for a variety of complicating factors, such as

• countermeasures by enemy ships,

• dense target environment, resulting in

• mis-associations,

• ISM classifier errors, etc.

Several scenario variants were examined to ascertain the advantages of using ISM reports andselected use of ESM reports, including a completely electro-magnetically silent version.

DS evidential reasoning for ID estimation was thus tested to the fullest, and was found tohandle these types of conflicts well. The randomness of each scenario run was ensured,particularly for ESM reports, and typical results were presented.

Suggestions for future research must include knowledge of the SSAR currently being installedon the CP-140 by MacDonald Dettweiler, in particular the rate and type of data that can beprocured, and the interfaces that can be provided to an outside MSDF function. This MSDFfunction should not interfere with the Data Management System (DMS) being fitted on theAurora by General Dynamics Canada, in the same way as a gateway was conceived andimplemented on the Halifax-class frigates to read the SHINPADS bus. A careful study of theDMS may change the scope of future studies as the type of data (contact, track, ID, etc.) mayevolve.


In addition to the above, it should be recognized that the Canadian Forces (CF) may changetheir priorities yet again in the light of an expanding NATO, and that deployment of the CP-140 will involve many more littoral operations, which the scenarios here only touched onlightly. Many of the formerly communist countries of Bulgaria, Estonia, Latvia, Lithuania,Romania, Slovakia and Slovenia, which are NATO members as of April 2004, border oneither oceans or large bodies of water, and are likely to fear invasion from the water.

Given all these caveats, one can still:

• re-evaluate the most appropriate missions for the CF and propose new scenarios, withspecial emphasis on locations for which GIS information can easily be retrieved.Other scenarios, which have worldwide acceptance (such as the North Atlantisscenario), can serve as alternatives.

• explore the context-sensitive use of multi-scan (or multi-frame) association,particularly for ESM reports in dense environments, to resolve the problem seen inthe DFS scenario for US ships, which are not easily resolved by a single-scanassociator.

• implement a wide variety of measures of performance, as was done on the naval sidefor ASCACT and COMDAT, but in the airborne case focussing primarily on IDestimation of vessels for the recognized maritime picture (RMP) (Dickinson, Martel& Jesion, 1997).

• increase the realism of the simulations by using the newly extended PDB populatedthrough a CSES task, which contains more than 2200 platforms.

• investigate performance in littoral operations by designing relevant scenarios typicalof Maritime Air Littoral Operations (MALO) and by including fixed land radar, aswell as movable targeting land radar, possibly following constraints imposed by theland geography.

• improve ESM simulations by allowing many emitters to be declared at a given timefor a given platform, and possibly implementing a scheme that would more closelyresemble the performance of a true ESM, i.e., reporting at fixed angular intervals,which the present artificial sensor implementation in CASE-ATTI cannot do.

• employ banks of filters such as in an interacting multiple model (IMM), but for fast-moving evading ships, such as could be present in the little-studied counter-drugoperations scenario. These banks of filters have been studied before in the navalcontext of fast manoeuvring aircraft (Bossé, Roy & Jouan, 1999).

• if the above IMMs are to be used for land targets also, they should include abruptstop-and-go motions as a distinct, probably very common, behaviour. Solutions of theIntelligence, Surveillance, Target Acquisition and Reconnaissance (ISTAR)Technology Demonstrator may be useful in this respect.

• procure and analyze SAR imagery generated on actual missions or exercises forselected targets, with the existing SSAR ISM for further validation (since theprocured version of the SAR can have different performance characteristics than thepreviously foreseen ADM version). If Stripmap imagery is available, this wouldcorrelate with an activity related to tracking and ID of land targets.


• try to obtain true FLIR imagery through video capture cards on the same actualmissions or exercises for the same selected targets.


7. References

Bar-Shalom, Y. & Fortmann, T.E. (1988). Tracking and Data Association, Academic Press,Mathematics in Science and Engineering, Vol. 179, ISBN 0-12-079760-7 (1988).

Bauer, M. (1997). Approximation Algorithms and Decision Making in the Dempster-ShaferTheory of Evidence-An Empirical study, International Journal of Approximate Reasoning,vol. 17, no. 2-3, pp. 217 237, 1997.

Boily, D., & Valin, P., (2000). Optimization and Benchmarking of Truncated Dempster-Shafer for Airborne Surveillance, NATO Advanced Study Institute on Multisensor and SensorData Fusion, Pitlochry, Scotland, United Kingdom, June 25 July 7, 2000 (Kluwer AcademicPublishers), NATO Science Series, II. Mathematics Physics and Chemistry Vol. 70, pp. 617-624.

Bossé, E. (2000). Identity Information Fusion Through Evidential Reasoning, NATOAdvanced Research Workshop on Multisensor Fusion, Pitlochry, Scotland, United Kingdom,June 25 July 7, 2000, NATO Science Series, II. Mathematics Physics and Chemistry Vol.70, Kluwer Academic Publishers, pp. 457-480 (published in 2002).

Bossé, E., Valin, P., & Jouan, A. (1999). A Multi-Agent Data Fusion Architecture for anAirborne Mission Management System, NATO Research and Technology Organization (RTO)SCI Symposium on Advanced Mission Management and System Integration Technologies forImproved Tactical Operations , 27-29 Sept. 1999, Florence, Italy, pp.9-1 to 9-12.

Bossé, E., & Roy, J. (1997). Measures of Performance for the ASCACT Multisensor DataFusion Demonstration Program, DRDC Valcartier report DREV TM-9618.

Bossé, E., Roy, J., & Jouan, A., (1999). Investigating the performance of some tracking filterschema for the Advanced Shipboard Command and Control Technology (ASCACT) project,DREV-TR-1999-208, October 2000.

Bossé, E., Roy, J., & Paradis, S., (2000). Modeling and simulation in support of the design ofa data fusion system, Information Fusion Volume 1, Issue 2, pp. 77-87, (December 2000).

Dickinson, R.G., Martel, S., & Jesion, A., (1997), Measures of Performance and Effectivenessfor Surface Surveillance, Project Report ORD-PR-1-97, 01 Aug 1997.

Drummond, O. E., Castanon, D.A. & Bellovin, M.S., (1990). Comparison of 2-D Assignmentfor Sparse, Rectangular, Floating Point, Cost Matrix, Journal of the SDI Panels on Tracking,Institute for Defense Analyses, no. 4, pp. 4-81 to 4-97, 1990.

Duclos-Hindié, N. et al. (1995). CASE-ATTI Sensor Module Programmer s Guide, version1.2, by Groupe INFORMISSION Inc., 1995.

Jonker, R. & Volgenant, A. (1987). A shortest augmenting path algorithm for dense andsparse linear assignment problems, Computing, Vol. 38, pp. 325-340 (fast implementation byCastanon)

Jouan, A., Gagnon, L., Shahbazian, E., & Valin P. (1998). Fusion of Imagery Attributes withNon-Imaging Sensor Reports by Truncated Demspter-Shafer Evidential Reasoning, in First


International Conference on Information Fusion, FUSION 98, Las Vegas, 6-9 July 1998, Vol.II, pp. 549-556.

Jouan, A., Valin, P., & Bossé, E. (1999). Testbed for Fusion of Imaging and Non-ImagingSensor Attributes in Airborne Surveillance Missions, Second International Conference onInformation Fusion, FUSION 99, 6-8 July 1999, Sunnyvale, CA, Vol. 2, pp. 823-830.

LM Canada. (1998). Document No. 990001008, MSDF Implementation and Test Document ,for Real-Time Issues and Demonstrations of Data Fusion Concepts for AirborneSurveillance , Year 1 of PWGSC Contract No. W7701-6-4081, final Rev.1 dated 27September 1999.

LM Canada (1999). Document No. 990001011, MSDF Implementation and Test Document,for Real-Time Issues and Demonstrations of Data Fusion Concepts for AirborneSurveillance , Year 2 of PWGSC Contract No. W7701-6-4081, final Rev.1 dated 27September 1999.

LM Canada (2000). Doc. No. 990001013, MSDF Design Document for Real-Time Issues andDemonstrations of Data Fusion Concepts for Airborne Surveillance, Year 3 of PWGSCContract No. W7701-6-4081, Rev. 0, 23 February 2000.

LM Canada (2001a). Document No. 990001234, Detailed Design Document - Parts 1 and 2,for Demonstrations of Image Analysis and Object Recognition Decision Aids for AirborneSurveillance , Contract No. W2207-8-EC01, 22 January 2001.

LM Canada (2001b). No. 990001236, Final Report for Demonstrations of Image Analysis andObject Recognition Decision Aids for Airborne Surveillance, Contract No. W2207-8-EC01,Rev. 0, 22 January 2001.

Miles, M. (1969). Wave Spectra Estimated from a Stratified Sample of 323 North AtlanticWave Records, NRC Laboratory Technical Report # LTR-SH-118A, 1969

Nasrabadi, N.M. (1998). Automatic Target Recognition Using Artificial Neural Networks,SPIE Short Course Notes #SC38, Bellingham, 1998

Nathanson, F.E. (1991). Radar Design Principles - Signal Processing and the Environment(2nd edition, with J.P. Reilly and M.N. Cohen)), McGraw Hill, Inc., ISBN 0-07-046052-3

Newman, J.N. (1977). Marine Hydrodynamics, MIT Press, Cambridge, 1977

Paradis, S., Roy, J., & Treurniet, W., (1998). Integration of all Data Fusion Levels Using aBlackboard Architecture, EuroFusion98, Great Malvern, UK, 6-7 October 1998.

Roy, J., & Bossé, É. (1998). A Generic Multi-Source Data Fusion System, DREV-R-9719,June 1998. UNCLASSIFIED.

Roy, J., Bossé, E., & Dion, D. (1995). CASE_ATTI: An Algorithm-level Testbed for Multi-Sensor Data Fusion, DRDC Valcartier report DREV-R-9411, May 1995.

Shahbazian, E., Bossé, E., Gagnon, L., Macieszczak, M., & Valin, P. (1998). Multi-AgentData Fusion Workstation (MADFW) Architecture, in Sensor Fusion: Architectures,Algorithms, and Applications II, SPIE Aerosense 98, Orlando, 13-17 April 1998, Proc. Conf.3376, pp. 60-68.


Shahbazian, E., Duquet, J.-R., Macieszczak, M., & Valin, P. (1998). A Generic Expert SystemInfrastructure for Fusion and Imaging Decision Aids, International Conference on DataFusion, EuroFusion98, Great Malvern, 6-7 October 1998, Vol. I, pp. 167-174.

Shahbazian, E., Duquet, J.-R., & Valin P. (1998). A Blackboard Architecture for IncrementalImplementation of Data Fusion Applications, in First International Conference onInformation Fusion, FUSION 98, Las Vegas, 6-9 July 1998,Vol. I, pp. 455-461.

Shahbazian, E., Gagnon, L., Duquet, J.-R., Macieszczak, M. & Valin, P. (1997). Fusion ofImaging and Non-Imaging Data for Surveillance Aircraft, in Sensor Fusion: Architecture,Algorithms, and Applications, SPIE Aerosense 97, Orlando, 20-25 April 1997, Proc. Conf.3067, pp. 179-189.

Simard, M.A., Valin, P. & Shahbazian, E. (1994). Fusion of ESM, Radar, IFF and otherAttribute Information for Target Identity Estimation and a Potential Application to theCanadian Patrol Frigate, AGARD 66th Symposium on Challenge of Future EW System Design,AGARD-CP-546, 18-21 October 1993, Ankara (Turkey), pp. 14.1-14.18, published May 1994.

Tessem, B. (1993). Approximations for efficient computation in the theory of evidence,Artificial Intelligence, vol. 61, pp. 315 329, June 1993.

Tunaley, J.K.E. (1981). Ship Motion and Its Effect on SAR Imagery, DRDC-O Report forContract # 0ST.36001-0-3319, 1981.

Valin, P., & Boily, D., (2000) Truncated Dempster-Shafer Optimization and Benchmarking,in Sensor Fusion: Architectures, Algorithms, and Applications IV , SPIE Aerosense 2000,Orlando, Florida, April 24-28, 2000, Vol. 4051, pp. 237-246.

Valin, P., & Bossé, E., (2003a). Fusion of Imaging and Non-Imaging, Sensor Information forAirborne Surveillance, SMi conference on "Military Data Fusion", London, England, 22-23Sept, 2003.

Valin, P., & Bossé, E., (2003b). Using a priori databases for identity estimation throughevidential reasoning in realistic scenarios, RTO IST Symposium on Military Data andInformation Fusion , Prague, Czech Republic, 20-22 October 2003.

Valin, P., Gagnon, L., Macieszczak, M., Shahbazian, E., & Bossé, E. (1998). TestbedArchitecture for Fusion of Imaging and Non-Imaging Airborne Sensors, in IGARSS '98,Seattle, 6-10 July 1998, Session C05, paper 1 (CD-ROM proceedings, ISBN 0-7803-4406-5).

Valin, P., & Jouan, A., (1999). DND / LM Canada / University Collaborations DevelopingTechnologies for Airborne Mission Management Systems, Canadian Aeronautics and SpaceInstitute (CASI) 46th Annual Conference, Montréal, 2-5 May 1999, pp. 705-712.

Valin, P., Jouan, A., & Bossé, E., (1999). Fusion of imaging and non-imaging sensorinformation for airborne surveillance, Sensor Fusion: Architectures, Algorithms, andApplication III, SPIE Aerosense 99, Orlando, 7-9 April 1999, Proc. Conf. 3719, pp. 126-138.

Valin, P., Jouan, A., Gagnon L. and Bossé, E., (1999). Airborne Fusion of Imaging and Non-Imaging Sensor Information for Maritime Surveillance, Quality Control by Artificial Vision(QCAV) '99, Trois-Rivières, 18-21 May 1999, pp. 281-286.


8. Acronyms

The list of acronyms presented here serves for all three documents: TM-281, TR-282 and TR-283.

AAM Air-to-Air MissileADM Advanced Development ModelAIFIE Attribute Information Fusion techniques for target Identity EstimationAIMP Aurora Incremental Modernization ProjectAIR Average ID RateAOP Area Of ProbabilityAR Auto-RegressiveASCACT Advanced Shipborne Command and Control TechnologyASM Air-to-Surface MissileAWACS Airborne Warning And Control SystemBB BlackBoardBPA Basic Probability AssignmentBPAM Bayesian Percent Attribute MissC2 Command and ControlC4I Command, Control, Communications, Computer and IntelligenceCAD Computer-Aided DesignCANEWS Canadian Electronic Warfare SystemCASE-ATTI Concept Analysis and Simulation Environment for Automatic Target

Tracking and IdentificationCCIS Command and Control Information SystemCCS Command and Control SystemCDO Counter-Drug OperationsCF Canadian ForcesCIO Communications Intercept OperatorCIWS Close-In Weapon SystemCL Confidence LevelCM Centre of MassCOMDAT Command Decision Aid TechnologyCOMINT Communications IntelligenceCOTS Commercial Off-The-ShelfCPF Canadian Patrol FrigateCPU Central Processing UnitCRAD Chief of Research And DevelopmentCSES Combat System Engineering ServicesCSIS Combat Support In-ServiceDAAS Decision Aids for Airborne SurveillanceDF Data FusionDFCP Data Fusion between Collaborating PlatformsDFDM Data Fusion Demonstration Model


DFS Direct Fleet SupportDM Data MileDMS Data Management SystemDPG Defence Planning GuidanceDRDC-O Defence R&D Canada OttawaDS Dempster-ShaferDSC Digital Scan ConverterEDM Engineering Development ModelEGI Embedded GPS and INSELINT Electronic IntelligenceELNOT ELINT NotationEMCON Emission ControlENL Emitter Name ListEO Electro-OpticESM Electronic Support MeasuresFLIR Forward Looking Infra-RedFM Frequency ModulationGIS Geographical Information SystemGPAF General Purpose Air ForcesGPDC General Purpose Digital ComputerGPL Geo-Political ListingGPS Global Positioning SystemHCI Human Computer InterfaceHLA High Level ArchitectureHW HardwareID IdentificationIFF Identification Friend or FoeIMM Interacting Multiple ModelINS Inertial Navigation SystemIR Infra-RedIRST Infra-Red Search and TrackISAR Inverse SARISIF International Society of Information FusionISM Image Support ModuleISTDS Internal System Track Data StoreJAIF Journal of Advances in Information FusionJDL Joint Directors of LaboratoriesJPDA Joint Probabilistic Data AssociationJVC Jonker-Volgenant-CastanonKBS Knowledge-Based SystemLAMPS Light Airborne Multi-Purpose SystemLAP Local Area PictureLM Lockheed MartinMAAO Maritime Air Area OperationsMAD Magnetic Anomaly DetectorMALO Maritime Air Littoral Operations


MARCOT Maritime Coordinated Operational TrainingMFA Multi-Frame AssociationMHP Maritime Helicopter ProjectMHT Multiple Hypothesis TrackingMIL-STD Military Standard (US)MOP Measure Of PerformanceMSDF Multi-Source Data FusionMSP Maritime Sovereignty PatrolMTP Maritime Tactical PictureNASO Non-Acoustic Sensor OperatorNATO North Atlantic Treaty OrganizationNAVCOM Navigation CommunicationNAWC Naval Air Warfare CenterNCW Network Centric WarfareNEOps Network-Enabled OperationsNILE NATO Improved Link ElevenNM Nautical MileNN Neural NetworkOMI Operator-Machine InterfaceOODA Observe, Orient, Decide, ActOR Object RecognitionOTHT Over-The-Horizon TargetingPDA Probabilistic Data AssociationPDB Platform Data BasePU Participating UnitPWGSC Public Works and Government Services CanadaR&D Research and DevelopmentRATT Radio TeleTypeRCMP Royal Canadian Mounted PoliceRCS Radar Cross-SectionRDP Range Doppler ProfilerRM Resource ManagementRMP Recognized Maritime PictureROI Region Of InterestRPM Revolutions Per MinuteSAM Surface-to-Air MissileSAR Synthetic Aperture RadarSARP SAR ProcessorSC Ship CategorySDC-S Signal Data Converter-StorerSHINPADS Shipboard Integrated Processing And Display SystemSKAD Survival Kit Air DroppableSL Ship LengthSNNS Stuttgart Neural Net SimulatorSNR Signal-to-Noise RatioSS Sea State


SSAR Spotlight SARSSC Surface Surveillance and ControlSSM Surface-to-Surface MissileST Ship TypeSTA Situation and Threat AssessmentSTANAG Standardization NATO AgreementSTIM StimulationSW SoftwareTACNAV Tactical NavigationTD Technology DemonstratorTDS Truncated DSTM Track ManagementUN United NationsUSC Underwater Surveillance and ControlVOI Volume Of InterestWAP Wide Area PictureXDM eXperimental Development Model


9. Annexes

9.1. General data/information fusion sourcesSince information/data fusion is an emerging science that incorporates elements of physics,engineering, mathematical physics, and computational science, the International Society ofInformation Fusion (ISIF) was created in 1999, with a constitution approved in April 2000.

For ISIF, information fusion encompasses the theory, techniques and tools conceived andemployed for exploiting the synergy in the information acquired from multiple sources(sensors, databases, information gathered by humans, etc.), such that the resulting decision oraction is in some sense better (qualitatively or quantitatively, in terms of accuracy, robustness,etc.) than would be possible if any of these sources were used individually without suchsynergy exploitation. In doing so, events, activities and movements will be correlated andanalyzed as they occur in time and space, to determine the location, identity and status ofindividual objects (equipment and units), to assess the situation, to qualitatively andquantitatively determine threats, and to detect patterns in activity that reveal intent orcapability. Specific technologies are required to refine, direct and manage the informationfusion capabilities.

The ISIF web site at http://www.inforfusion.org contains much of the crucial documentationin the whole domain. The results contained in this series of reports were presented in part atthe first seven ISIF-sponsored FUSION conferences in

2004: Stockholm, Sweden, at http://www.fusion2004.org/

2003: Cairns, Queensland, Australia at http://fusion2003.ee.mu.oz.au/

2002: Annapolis, Maryland, USA athttp://www.inforfusion.org/Fusion_2002_Website/index.htm

2001: Montreal, Quebec, Canada at http://omega.crm.umontreal.ca/fusion/, with bothLockheed Martin Canada and DRDC Valcartier as sponsors

2000: Paris, France, at http://www.onera.fr/fusion2000/

1999: Sunnyvale, California, USA, at http://www.inforfusion.org/fusion99/, duringwhich the concept of ISIF first emerged

1998: Las Vegas, Nevada, USA, at http://www.inforfusion.org/fusion98/

The eighth such conference was held in Philadelphia, Pennsylvania, USA, on July 25-28,2005 (see http://www.fusion2005.org/ for more details).

In addition, summaries were presented internationally for NATO through their ResearchTechnology Agency (RTA) symposia and their Advanced Study Institutes (ASI). Othervenues where this research work was promulgated include the SPIE Aerosense series held inOrlando each year, and various other conferences. The SPIE Aerosense series has recentlybeen renamed SPIE Defense & Security Symposium.

The ISIF community is also served by the Information Fusion Journal published by Elsevier(see http://www.elsevier.com/wps/find/journaldescription.cws_home/620862/description formore information), and has an on-line journal of its own, the Journal of Advances in

http://www.inforfusion.org

http://www.fusion2004.org/

http://fusion2003.ee.mu.oz.au/

http://www.inforfusion.org/Fusion_2002_Website/index.htm

http://omega.crm.umontreal.ca/fusion/

http://www.onera.fr/fusion2000/

http://www.inforfusion.org/fusion99/

http://www.inforfusion.org/fusion98/

http://www.fusion2005.org/

http://www.elsevier.com/wps/find/journaldescription.cws_home/620862/description


Information Fusion (JAIF), with information on submissions athttp://www.inforfusion.org/JAIF-CFP-Oct28.htm.

As for the documentation specifically needed for this report, the section entitled Referencescontains the complete list.

9.2. Specific related data/information fusion sourcesThe contents of this series of three reports is based on two contracts entitled

• Demonstrations of Data Fusion Concepts for Airborne Surveillance , and

• Demonstrations of Image Analysis and Object Recognition Decision Aids forAirborne Surveillance ,

with the following 14 deliverables (the date of the first publication of each report is shown,and the date of the final revision where applicable):

1. LM Canada Doc. No. 990001006, (1997). MSDF Requirements SpecificationDocument for Year 1 of PWGSC Contract No. W7701-6-4081 on Real-Time Issuesand Demonstrations of Data Fusion Concepts for Airborne Surveillance (andreferences therein), final Rev.1 dated 27 September 1999.

2. LM Canada Doc. No. 990001007, (1997). MSDF Design Document for Year 1 ofPWGSC Contract No. W7701-6-4081 on Real-Time Issues and Demonstrations ofData Fusion Concepts for Airborne Surveillance (and references therein), final Rev.1dated 27 September 1999.

3. LM Canada Doc. No. 990001008, (1998). MSDF Implementation and Test Documentfor Year 1 of PWGSC Contract No. W7701-6-4081 on Real-Time Issues andDemonstrations of Data Fusion Concepts for Airborne Surveillance (and referencestherein), final Rev.1 dated 27 September 1999.

4. LM Canada Doc. No. 990001009, (1998). MSDF Requirements SpecificationDocument for Year 2 of PWGSC Contract No. W7701-6-4081 on Real-Time Issuesand Demonstrations of Data Fusion Concepts for Airborne Surveillance (andreferences therein), final Rev.1 dated 27 September 1999.

5. LM Canada Doc. No. 990001010, (1998). MSDF Design Document for Year 2 ofPWGSC Contract No. W7701-6-4081 on Real-Time Issues and Demonstrations ofData Fusion Concepts for Airborne Surveillance (and references therein), final Rev.1dated 27 September 1999.

6. LM Canada Doc. No. 990001011, (1999). MSDF Implementation and Test Documentfor Year 2 of PWGSC Contract No. W7701-6-4081 on Real-Time Issues andDemonstrations of Data Fusion Concepts for Airborne Surveillance (and referencestherein), final Rev.1 dated 27 September 1999.

7. LM Canada Doc. No. 990001012, (2000). MSDF Requirements SpecificationDocument for Year 3 of Contract No. W7701-6-4081 on Real-Time Issues andDemonstrations of Data Fusion Concepts for Airborne Surveillance (and referencestherein), Rev. 0, 23 February 2000.

http://www.inforfusion.org/JAIF-CFP-Oct28.htm


8. LM Canada Doc. No. 990001013, (2000). MSDF Design Document for Year 3 ofPWGSC Contract No. W7701-6-4081 on Real-Time Issues and Demonstrations ofData Fusion Concepts for Airborne Surveillance (and references therein), Rev. 0, 23February 2000.

9. LM Canada Doc. No. 990001014, (2000). MSDF Implementation and Test Documentfor Year 3 of PWGSC Contract No. W7701-6-4081 on Real-Time Issues andDemonstrations of Data Fusion Concepts for Airborne Surveillance (and referencestherein), Rev. 1, 20 March 2000.

10. LM Canada DM No. 990001234-a, (2001). Detailed Design Document - Part 1,Demonstrations of Image Analysis and Object Recognition Decision Aids forAirborne Surveillance, Contract No. W2207-8-EC01, Rev. 0, 22 January 2001.

11. LM Canada DM No. 990001234-b, (2001). Detailed Design Document - Part 2,Demonstrations of Image Analysis and Object Recognition Decision Aids forAirborne Surveillance, Contract No. W2207-8-EC01, Rev. 0, 22 January 2001.

12. LM Canada DM No. 990001235-a, (2001). Testing and Benchmarking IMM-CVCA vsKalman Filtering, Demonstrations of Image Analysis and Object RecognitionDecision Aids for Airborne Surveillance, Contract No. W2207-8-EC01, Rev. 0, 22January 2001.

13. LM Canada DM No. 990001235-b, (2001). Testing and Benchmarking Ship Classifierfor SAR Imagery, Demonstrations of Image Analysis and Object RecognitionDecision Aids for Airborne Surveillance, Contract No. W2207-8-EC01, Rev. 0, 22January 2001.

14. LM Canada DM No. 990001236, (2001). Final Report, Demonstrations of ImageAnalysis and Object Recognition Decision Aids for Airborne Surveillance. ContractNo. W2207-8-EC01, Rev. 0, 22 January 2001.

It should be pointed out that this last report in the series of three DRDC Valcartier reportsuses nearly all the results of the implementation and test documents of the first two years ofthe contract on Real-Time Issues and Demonstrations of Data Fusion Concepts for AirborneSurveillance. Nearly all the figures illustrating results (as well as the tables) that do notappear in open literature publications come from these documents, and have been identified assuch in the text and in the references section. The description of the test bed and the agentimplementation on the KBS BB come mainly from the contract on Demonstrations of ImageAnalysis and Object Recognition Decision Aids for Airborne Surveillance .


9 Distribution list

INTERNAL DISTRIBUTION

DRDC Valcartier TR 2004-283

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GB Lewis, DAEPMM 5

1 - Evolved Sea Sparrow Missile Project Manager

1 - Halifax Modernized Command and Control SystemProject Manager

1 - Canadian Forces Command and Staff College Toronto Attn: Commanding Officer

1 - Canadian Forces Maritime Warfare School CFB Halifax Halifax, Nova Scotia Attn: Commanding Officer


EXTERNAL DISTRIBUTION (cont d)


2 - Canadian Forces Maritime Warfare Centre CFB Halifax, NS Attn: TAC AAW OIC Modeling and Simulation

2 - Canadian Forces Naval Operations School CFB Halifax, NS Attn: Tactics CT AWW

1 - Canadian Forces Naval Engineering School CFB Halifax, NS Attn: CSST

1 - Operational Requirements Analysis Cell CFB Halifax, NS Attn: Commanding Officer

1 - Canadian Forces Fleet School CFB Esquimalt, BC Attn: Commanding Officer/WTD

1 - Operational Requirements Analysis Cell CFB Esquimalt, BC Attn: Commanding Officer

dcd03e rev.(10-1999)

UNCLASSIFIED SECURITY CLASSIFICATION OF FORM

(Highest Classification of Title, Abstract, Keywords)

DOCUMENT CONTROL DATA

1. ORIGINATOR (name and address) Defence R&D Canada Valcartier 2459 Pie-XI Blvd. North Quebec, QC G3J 1X5

2. SECURITY CLASSIFICATION (Including special warning terms if applicable) Unclassified

3. TITLE (Its classification should be indicated by the appropriate abbreviation (S, C, R or U) Demonstration of data/information fusion concepts for airborne maritime surveillance operations (U)

4. AUTHORS (Last name, first name, middle initial. If military, show rank, e.g. Doe, Maj. John E.) Valin, P., Jouan, A., Bossé, E.

5. DATE OF PUBLICATION (month and year) May 2006

6a. NO. OF PAGES 87

6b .NO. OF REFERENCES 40

7. DESCRIPTIVE NOTES (the category of the document, e.g. technical report, technical note or memorandum. Give the inclusive dates when a specific reporting period is covered.)

Technical Report

8. SPONSORING ACTIVITY (name and address)

9a. PROJECT OR GRANT NO. (Please specify whether project or grant) 13DV

9b. CONTRACT NO.

10a. ORIGINATOR’S DOCUMENT NUMBER TR 2004-283

10b. OTHER DOCUMENT NOS

N/A

11. DOCUMENT AVAILABILITY (any limitations on further dissemination of the document, other than those imposed by security classification)

Unlimited distribution Restricted to contractors in approved countries (specify) Restricted to Canadian contractors (with need-to-know) Restricted to Government (with need-to-know) Restricted to Defense departments Others

12. DOCUMENT ANNOUNCEMENT (any limitation to the bibliographic announcement of this document. This will normally correspond to the Document Availability (11). However, where further distribution (beyond the audience specified in 11) is possible, a wider announcement audience may be selected.)

UNCLASSIFIED

SECURITY CLASSIFICATION OF FORM (Highest Classification of Title, Abstract, Keywords)

dcd03e rev.(10-1999)

UNCLASSIFIED SECURITY CLASSIFICATION OF FORM

(Highest Classification of Title, Abstract, Keywords)

13. ABSTRACT (a brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is highly desirable that the abstract of classified documents be unclassified. Each paragraph of the abstract shall begin with an indication of the security classification of the information in the paragraph (unless the document itself is unclassified) represented as (S), (C), (R), or (U). It is not necessary to include here abstracts in both official languages unless the text is bilingual).

The objective of this report is to demonstrate the achieved performance of judiciously selected data/information fusion and object recognition algorithms for realistic sensor simulations in relevant airborne maritime surveillance missions. The testbed architecture is based on a Knowledge Based System (KBS) developed by LM Canada in collaboration with DRDC-V. The chosen Multi-Sensor Data Fusion (MSDF) and Image Support Module (ISM) algorithms are agents on this data driven KBS. Complete results are presented for the Maritime Air Area Operations (MAAO) and Direct Fleet Support (DFS) scenarios for a variety of complicating factors, such as countermeasures, dense target environment, missed associations, ISM classifier errors, etc. Several scenario variants are examined to ascertain the advantages of using ISM reports and selected use of ESM reports including an electro-magnetically silent version. The Dempster-Shafer evidential reasoning for identity (ID) estimation is thus tested to the fullest, and is found to handle these types of conflict well. Final conclusions are presented and suggestions for future research are made.

14. KEYWORDS, DESCRIPTORS or IDENTIFIERS (technically meaningful terms or short phrases that characterize a document and could be helpful in cataloguing the document. They should be selected so that no security classification is required. Identifiers, such as equipment model designation, trade name, military project code name, geographic location may also be included. If possible keywords should be selected from a published thesaurus, e.g. Thesaurus of Engineering and Scientific Terms (TEST) and that thesaurus-identified. If it is not possible to select indexing terms which are Unclassified, the classification of each should be indicated as with the title.)

Information fusion, data fusion, CP-140 Aurora, maritime surveillance, scenarios, Dempster-Shafer, demonstration, robustness, Knowledge Based System, blackboard, Maritime Air Area Operations, Direct Fleet Support, Image Support Module, classifiers, SAR, ESM, identity.

UNCLASSIFIED

SECURITY CLASSIFICATION OF FORM (Highest Classification of Title, Abstract, Keywords)

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demonstration of data/information fusion concepts for ....../¶objectif de ce rapport est la...

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